1 Introduction

The innovations brought about by the convergence of the Internet of Things (IoT) with the healthcare sector have resulted in a sharp increase in interest in the Internet of Medical Things (IoMT) over the past ten years. The cyber-physical system (CPS) is essential for balancing this equation since it offers a multi-dimensional scheme that primarily takes into account the industrial prospects across the network [1, 2]. Create a budget-friendly atmosphere for electronic healthcare systems as a result. In fact, cyber-physical systems can be used in pharma, pharmaceutical, telehealth, and other healthcare applicational environments [1,2,3]. Here is a highlighted list of popular run-time programs [4, 5]: (i) HealthTap; (iv) MyChart; (v) Pocket Pharmacist; (ii) Medisafe Medication; and (iii) Teladoc Health. But in terms of applicational connectivity, telemediation, virtual diagnosis and cost-effectiveness, and stakeholder registration verification and validation, the evolution of cyber-physical systems in the healthcare sector makes a significant impact. Moreover, this technology integrates digital-to-analog and analog-to-digital components, as well as logical and physical systems working together to manage intercommunication transmission [4, 6]. Actuators, wireless network sensors, and networking modules are all part of the cyber-physical system network, which aids in the management of suitable automation, particularly in the healthcare platforms [5, 6].

In general, IoT requires integral support of cyber-physical systems in a healthcare environment, which is considered a complex prospect where the external operations are performed on cyber applications, as shown in Fig. 1. Undoubtedly, this integrated manner not only provides information and communication technology (ICT) progressive. On the other side, it also helps in more positive fluctuation in the acquisition of data transmission, management, organization, preservation, and optimization. On the other end, cybersecurity is considered in terms of major challenging issues, where vulnerability can be measured, such as intrusion hazards, malicious attacks, or attempts of malicious insiders [4, 6]. These days, the experts of CPS are highly concerned about the privacy and security of the technology, especially in collaborative prospects with electronic healthcare. However, the intrusion detection system (IDS) performs a vital role, where intrusion detection is one of the key applications that maximize the integrity of the system. Recently, most of the IDS has been employed for effective and efficient prevention of malicious attacks [6, 7]. The working operation is to classify anomalies, where IDS is categorized into two parts, such as identifying misuse predictions and analysis of anomaly occurrence in the running environment. In both aspects, the feature of similarities in terms of malicious attacks examination, which helps in misuse prediction and evaluation. Nowadays, every auditable information requires an authenticated dataset, which needs to be associated with it in order to examine the impact of intrusions [6,7,8]. In fact, a misuse detector generates a minimized effect in the function point. However, the detector has different types of challenging prospects, like working with another line of defense, identifying intrusions that are unable to be adopted in the evaluation of security procedures, and no role of safeguard integration.

Fig. 1
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Current environment of data processing using cyber-physical systems and Internet of Things

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On the other end, the detection of anomalies in IDS is constructed to evaluate a routine profile behavior with a categorized one, where marching the usual behavior and separate unusual activities, such as malicious attacks [7,8,9]. Comparatively, IDS-enabled anomaly detection performs better but is not accurate enough to say that it finds unknown malicious activity with a hundred percent ratio. However, this technology interconnects a broad range of ubiquitous devices, where the management of computational resources fluctuates, along with the cycle of battery consumption, transmission protocols, software connectivity, and operation of deliverance [8, 9]. Such type of device heterogeneity makes this system more limited, including the placement of security challenges, and the design of the surface raises the rate of attacks in the recent environment. Whereas the adaptation of blockchain technology makes these differences more lesser in terms of providing a distributed environment with node heterogeneity connectivity [8, 10]. Due to this, the system can fetch overall vulnerability occurrences and resist them for future endorsement.

In the recent healthcare environment, machine learning (ML) techniques have been presented for examining patterns from collected data, and then effectively identifying and detecting points of interest in terms of cybercriminal activities effectively [10, 11]. However, it affects, while loading a large number of datasets, where the efficiency scale and their point of revealed prospects cannot be accomplished the mark, including a low performance for identifying malicious attacks when nodes of the network are in distributed mode. On the other side, deep learning (DL) models stimulate such identification patterns in a complex way, but the results count as sophisticatedly [11, 12]. Although the complex network of DL requires more computation power, experts can rely on the generated results due to its reliability and efficiency. In fact, the experts of AI majorly focus on the investigation of the malicious ubiquitous; in order to provide a novel design that fulfills a trustworthiness environment, which supports cybercrime-enabled behavioral profiling analysis [13,14,15]. In addition, node reputation is evaluated as another prospect that needs to be resolved while applying a list of attacks detection and recognition because it violates the Euclidean distance measurement between profiles.

1.1 Research motivation and objectives

This study examines all of these difficult possibilities, but it primarily focuses on creating a secure infrastructure for IoMT. To ensure privacy, security, and automated decision-making using blockchain and machine learning, data must be stored, shared, and organized in a distributed node-to-node environment. This paper proposes a novel concept for a distributed consortium network in which member nodes build intercommunication. The ML-enabled SVM algorithm has a significant impact on this process. SVM’s primary responsibility during interconnectivity is to handle crucial situations including data processing, resource management, scalability, and security. This article thus discusses three important issues that arise in the current e-healthcare interoperable environment: peer-to-peer communication between nodes connected to the same network, easy data protection and confidentiality maintenance, and the general security of the health infrastructure. Through the proposed architecture, this study offers a novel, interoperable method to preserve these. The main contribution of this research is expounded upon in the following definition of the research objectives and contributions argument:

  • A list of research gaps that are assessed throughout the problem-solving process is provided in this publication. These gaps have been compiled from a variety of reputable academic research publisher sources, including IEEE, ACM, Elsevier, Springer, Wiley, and Taylor & Francis.

  • "BDLT-IoMT" is a suggested secure architecture for the Internet of Medical Things (IoMT) that combines ML and blockchain DLT. Furthermore, one of the main functions of this suggested architecture is that blockchain is essential for supplying training data for machine learning models, like support vector machines (SVMs), which in particular seek to arrange, distribute, and trade data from dispersed storage in order to ensure security, protection, and automation in decision-making.

  • To facilitate communication between participating nodes and handle both technologies simultaneously, a consortium network is created with the goal of offering a channel for data processing, resource management, scalability, and security.

  • As a result, the suggested design offers peer-to-peer communication between nodes, seamless data integrity, and infrastructure security while addressing issues with platform interoperability.

  • A list of issues related to the deployment of distributed applications (DApps) is provided, together with a justification statement and an explanation of potential remedies.

1.2 Outline of this research work

The further description of this paper is aligned and presented as follows: In Sect. 2, the detailed argument based on exiting blockchain applications running, infrastructural weakness, and protocols, along with the provisional statement of IoMT integrations are discussed. However, Sect. 3 presents the working objective of the proposed study, along with the activities of executions. The brief discussion of the proposed architectural simulations and results in Sect. 4, whereas a list of implementation, deployment, and further research gaps that have emerged in the organization of this work in Sect. 5. At the end, this paper concludes with the well-defined statement of conclusion in Sect. 6.

2 Related work

Recently, most of the running systems of e-healthcare considered as highly consumed energy resources due to the association of IoT and connectivity. Undoubtedly, it fulfills a basic form of industrial healthcare ecosystem but requires more in-depth investigation to overcome it. Considering the ad hoc nature of this technology is one of the reasons that the emergent list of threats needs to be captured and estimated in real time, including botnets [16]. This may be raised while collaborating edge devices with the host IoT devices for designing successful cyber-physical systems for making cybersecurity prospects that minimize transmission overhead and energy consumption. However, the existing proposal received on the development of an effective trust-based e-healthcare platform that follows the concept of autonomous vehicular network [16, 17]. It is one of the first trusted proposal received by the technology in 2021, which aim to assist associative methods of AI for autonomous driving vehicles, where assisted data can be exploited for calculating the exact trust values. After the integration of reinforcement learning, these healthcare-based autonomous driving vehicles stimulate a self-warning alert and report vulnerabilities. However, further details regarding the technological developments are addressed in the next subsections as follows:

2.1 Existing blockchain applications, infrastructure, and protocols

These days, the development of blockchain distributed ledger technology (BDLT) creates new paradigms, especially the topology of healthcare network management which has changed, including stakeholder registration, adding new data, and updating records in a decentralized manner [18, 19]. Undoubtedly, blockchain enhances information security and privacy procedures, integrity, confidentiality, trustworthiness, provenance, transparency, and platform interoperability. While distributed nodes are connected like patients are interconnected in the designed ecosystem networks via ubiquitous devices. However, the bibliometric analysis of blockchain in the health industry is quite limited. It is because a rate of growing bodies examines a potential fluctuation received by this collaborative technology. During the investigation, we found a lack of technical improvement received by the blockchain-enabled healthcare technology, majorly because of the high theoretical description available compared to real-time implementation. However, Table 1 presents an investigational report that highlights what factors still emerge as current research gaps, which can be transformed into future developments and maybe research trends. The evaluation metrics of this table are mentioned as follows: (i) a list of references, (ii) a major research gaps analysis, (iii) a list of research contributions, (iv) targeted research objectives, and (v) answered research questions.

Table 1 Current research gaps on the Internet of Medical Things environment and the role of blockchain
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2.2 Security hierarchy in Medical Internet of Things (IoMT) and the role of machine learning

Recent developments have shown that healthcare data management systems encounter problems like data availability, central storage, grant access, and operational controls. But not fulfill the requirements of advanced digital technology, including integrity, traceability, provenance, data transparency, immutability, flexible access controls, audit, trustworthiness, and privacy protection. However, the revolution of BDLT resolves the mentioned challenges, but there is a need to specify resource usage due to high computing requirements for managing a distributed environment; this technology suffers in terms of balancing the allocation of computational resources [26,27,28,29]. Undoubtedly, blockchain technology establishes confidence in the health hierarchy for data organization by enabling the tracking of changes from a collection of data sources and related forms. Current case studies elaborate on the importance of blockchain as a range of diverse fulfillment of health applications. The need is to address critical the concept of rescheduling the limited capability of computational resource optimization so that blockchain can be adapted successfully in every domain of the health industry. To overcome these challenging prospects, this paper investigates the factors affecting of possible adaptation of blockchain and their role in cost-efficient data management and organization is highlighted as follows:

  • Use of Hyperledger technology

  • Design cost-effective function in smart contracts

  • Customize consensus mechanisms

  • Define blockchain protocols

  • Association of NuCypher Re-Encryption mechanism

  • Immutable storage and connectivity

  • Specify communication channels

3 Research material and methodology

3.1 Problem description, formation, and notations

With the use of SVM, we can handle the issues of data processing, resource management (especially computational cost), and scalability, as shown in Fig. 2; due to this, we design a function that follows the mentioned constraints, such as the calculation of the distance of data points, reflect false negative and false positive values, and the margin data values. Here we explain this in a mathematical manner:

Fig. 2
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Working cycle of data processing and memory management

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\(f\left(a\right)=w*A+b^{\prime}\le -1\); for all false positive.

\(f\left(b\right)=w*B+b^{\prime}\ge 1\); for all false negative. Where two constraints are taken together, we can achieve to simplify the calculation of both constraints into 1. Let us assume the negative value y = − 1 and the positive value y = 1, as mentioned in Table 2.

Table 2 Notations
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In order to evaluate every point in terms of classifying correctly, the equation is designed as follows:

$$y^{\prime } \left( {w^{\prime } *\left( {A*B} \right) + b^{\prime } } \right) \ge 1$$

To maintain computational resources, the total cost of data processing is scheduled with this equation as follows:

$$(a2 - a1)*(w^{\prime } /w)$$

where \((a2*w^{\prime } - a1*w^{\prime } )/w\)

Here a2 and a1 are the variables that define positive data processing hierarchy and loss function in accordance with the designed resource limitations as follows: \({y}^{\prime}*(2*(f\left(a\right)+f\left(b\right))=1\)

For positive data processing, y must be equal to 1. Here, we define the possibilities of memory scalability during processing as follows: \(1*(w*a1+b^{\prime})=1\);where \(w^{\prime } *a1 = 1 - b^{\prime }\);

By adding both equations together to achieve data optimization, along with organization and management (as shown in Fig. 3), we present the equation as follows:

Fig. 3
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Cycle for achieving data optimization and memory hierarchy

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$$(\left(1-{b}^{\prime}\right)-(-{b}^{\prime}-1))/w$$
$$\frac{\left(1-{b}^{\prime}\right)+\left({b}^{\prime}+1\right)}{w}=\frac{2}{w}=f(a)$$

Hence, the maximum scalability that the proposed architecture can be handled is defined as follows:

$${\text{max}}\left( {w^{{\prime \prime }} ,b^{{\prime \prime }} } \right) = \frac{2}{w}{\text{such}}\;{\text{that}}\;y^{\prime } (w^{\prime } *\left( {A*B} \right) + b^{\prime } )^{{\prime \prime }} \ge 1$$

However, the minimization of memory scalability is integrated and illustrated as follows:

$${\text{min}}\left( {w^{{\prime \prime }} ,b^{{\prime \prime }} } \right) = \frac{w}{2} + ({\text{Sum}}\;{\text{of}}\;{\text{value}}\;{\text{c}})$$

Effective data classification, especially in binary classification tasks, is the SVM model’s primary strength. When combined with blockchain, the model benefits from the additional security, transparency, and trust it provides, fortifying and improving the dependability of the entire data pipeline from training to deployment. The security and verifiability of the decisions and updates made by SVM-based systems are additionally ensured by the decentralized and immutable nature of blockchain.

3.2 Proposed architecture and working sequences

A proposed distributed application (DApp) for Fig. 3 serves as a mediator between several stakeholders and the BDLT infrastructure. Maintaining privacy security procedures when Internet of Medical Things (IoMT) transactions are planned for transmission in an economical way, which is chosen by the system’s patients, is the main goal of such an implementation. But in order to ensure seamless transmission, this suggested BDLT-IoMT created a consortium network, with an administrator tasked with looking into any instances of fault tolerance that may arise during the processing cycle. Conversely, as illustrated in Fig. 3, two distinct channels—referred to as off-chain and on-chain—are suggested in order to rearrange the list of explicit and implicit transactions.

The BDLT-IoMT architecture, which combines blockchain technology with AI-enabled machine learning to avoid cyberattacks, is presented in this section and is illustrated in Fig. 4. Three sections cover the explanation of this proposed work: (i) ML-enabled SVM association and implementation; (ii) blockchain infrastructure solution; and (iii) IoT connectivity to facilitate health transactions. The first section of our proposed work relies heavily on ML-enabled SVM. Here, we performed data scheduling, resource management, and scalability tasks so that real-time captured data could be extracted, examined, filtered, aggregated, analyzed, and stored in an immutable blockchain that was predefined, like InterPlanetary File Storage (IPFS), as illustrated in Fig. 3. As a result of its implementation, the suggested BDLT-IoMT is able to meet the following three main restrictions (as shown in Fig. 4): infrastructure security (i) to protect automated decision-making; integrity (ii) to allow for smooth data sharing and exchange; and network resource optimization (iii) to enable smooth communication between disparate devices.

Fig. 4
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Proposed BDLT-IoMT architecture

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But as Fig. 5 illustrates, the technology behind blockchain is broken down into nine distinct sub-components: node interconnectivity, REST API, intercommunication channels, states, chaincode, consensus mechanism, proof-of-work (PoW) integration, and digital signature. The transaction processor manages every step of the BDLT hierarchy that has been described. Node interconnectivity offers a framework for connecting various nodes based on block size, where blocks are derived from transaction data, size, public key, hashing (n − 1) SHA-256, and hashing (n). REST API, on the other hand, plans transactions as they are completed, as mentioned in Table 3. Conversely, as was already said, intercommunication can be divided into two categories: on-chain and off-chain. The BDLT infrastructure (predefined) manages each individual transaction’s state when a new transaction is listed.

Fig. 5
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Steps of BDLT

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Table 3 Pseudocode of the proposed BDLT-IoMT
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Table 3 provides a concise description of the consensus policy and chaincode working objective along the digital signature procedure.

4 Simulations and results

The originality of the proposed work is discussed in this section with regard to the presentation of simulations and their distinct outcomes. To test the overall infrastructural security with regard to data preservation, we have divided the simulations into six distinct scenarios. These include the working cycle of BDLT-IoMT evaluation—false positive (Tests 1 and 2), BDLT-IoMT evaluation—false negative (Tests 1 and 2), the working cycle of BDLT-IoMT resource management (Test 1), the working cycle of BDLT-IoMT resource management (Test 2), and the overall infrastructural security test with regard to data protection throughout. Prior to commencing these tests, the following prerequisite must be satisfied:

  • System requirement—13th generation core i7 vPro processor is used, along with the 3.0 GHz clock speed.

  • 32 GB main memory is installed with the connectivity of 1 TB SSD.

  • Integrated/shared GPU is mandatory.

  • 10–100 Mbps Network bandwidth is required.

  • Software requirement—JavaScript installation, Truffle, Ganache, visual studio code, and additional plugins to support JavaScript program execution is mandatory.

Figure 6 illustrates the simulation result of the proposed BDLT-IoMT working cycle in terms of data processing. This scheduled test is based on both perspectives, like false positive—Test 1 (as shown in Fig. 6a) and false negative—Test 1 (as shown in Fig. 6b), where the metric of evaluation is the fluctuation received in the data processing cycle with respect to scheduling data per second (s). However, the analytics of false positive is the sum of 277 cycles per 3511 s, which is equal to 0.0788 cycles of data investigated per second. The uniqueness of these results shows that the proposed BDLT-IoMT is processing data better as compared to the previously published methods [30,31,32,33].

Fig. 6
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Working cycle of BDLT-IoMT (Test 1), where the metrix is the fluctuation receives in data processing cycle and scheduling data per second, a test of false positive, and b test of false negative

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Figure 7 shows the result of the proposed BDLT-IoMT simulations, which are based on the data processing cycle and related hierarchy. The test is conducted on two points of manner, like false positive—Test 2 (as shown in Fig. 7a) and false negative—Test 2 (as shown in Fig. 7b), where the metric of evaluation is the fluctuation received in the data processing cycle with respect to scheduling data per second (s). However, the analytics of false positive is the sum of 256 cycles per 2503 s, which is equal to 0.1022 cycles of data investigated per second. (Where the meaning of 2 is the second test, P is the false positive, and N is the true negative.)

Fig. 7
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Working cycle of BDLT-IoMT (Test w), where the metrix is the fluctuation receives in data processing cycle and scheduling data per second, a test of false positive, and b test of false negative

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The overall infrastructural security test is presented in Fig. 8, where evaluation is conducted twice to investigate the successful deployment of the proposed BDLT-IoMT, which mainly highlights the role of blockchain-enabling technology for data preservation prospects. The examination metric of this simulation is the sum of the cycle of resource management used with respect to data management and preservation slots delivered per second(s).

Fig. 8
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Overall infrastructural security solution using blockchain-enabling technology for data preservation prospects

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Decentralized machine learning environments, where preserving data integrity is crucial, were considered in the simulation. This represents real-world applications where data provenance and tamper resistance (as guaranteed by blockchain) are crucial, including those in the financial, legal, or medical records sectors. By ensuring that the simulations account for constraints, real-world scenarios, and data complexities, we believe that the results can be safely extended to real-world settings. We demonstrate that the simulated results translate well into broader, real-world contexts with our plans for field testing and matching simulation conditions with real applications.

However, Fig. 9 illustrates the simulation test of the complete cycle of computational resource consumption (Test 1), where the investigation metrics is the sum of fluctuation received in resource management during scheduling data and related hierarchy with respect to time (s). Whereas the total number of fluctuations received is 221 cycles in the 1500 s, which is a total of 0.1473 cycle/s. On the other side, the result of Fig. 10 (Test 2) is the sum of fluctuations received, is 243 cycles in 727 s, which is a total of 0.3342 cycle/s.

Fig. 9
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Working cycle of BDLT-IoMT for resource management Test 1

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Fig. 10
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Working cycle of BDLT-IoMT for resource management Test 2

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The combined outcomes of Tests 1 and 2 across the board for all three environments are displayed in this scenario, including enhanced infrastructure security, higher network resource consumption, and higher integrity of smooth data transfer by up to 1.87%, 1.37%, and 1.56%, respectively.

Overall infrastructural security test is conducted via the proposed BDLT-IoMT, as shown in Fig. 8, where evaluation is scheduled only once to investigate the successful deployment of the work, which mainly highlights the pop-up of the use of blockchain technology in the data protection scenario. Whereas the evaluation criteria are mentioned as the sum of the cycle of resource management used with respect to data management and preservation slots delivered per second(s), where 266 cycles are managed in 683 s (Fig. 11).

Fig. 11
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Overall infrastructural security solution using blockchain-enabling technology for data protection scenario

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However, the list of state-of-the-art publications is mentioned that are used as the comparative analysis as follows [30,31,32,33,34,35]:

  • A blockchain-based federated learning mechanism for the privacy preservation of healthcare IoT data;

  • A blockchain-based federated artificial intelligence system of intrusion detection for IoT healthcare system;

  • An original research article on a blockchain-based secure Internet of Medical Things framework for smart healthcare;

  • Efficient personal health records sharing on the Internet of Medical Things using searchable symmetric encryption, blockchain, and IPFS; and

  • Blockchain-based AI model for industrial healthcare applications

Tables 4 and 5 present a report of comparative analysis between the proposed BDLT-IoMT and other state-of-the-art methods, where the context of evaluation is addressed as follows: (i) data processing cycle, (ii) computational cost, (iii) memory scalability, (iv) trustworthiness environment, and (v) overall efficiency and accuracy.

Table 4 Report of systematic analysis (1)
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Table 5 Report of systematic analysis (2)
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5 List of limitations in implementation, deployment, and the current research gaps

In this section, we present a report that is based on a critical investigation raised during the implementation of this proposed BDLT-IoMT, where the major prospects of design and deployment are highlighted. In addition, this paper tries to provide a possible solution to the mentioned problems, which need to be fulfilled near future and most probably require technological maturity.

5.1 Seamless e-healthcare data collection and management

Due to the high demand of healthcare, the usage of interoperable platform is going on peak day-to-day and their integration-seamless information share and exchange between hospital-to-hospital or hospital-to-patient and vice versa, within consortium network, and even cross-border transactions. In this whole scenario, an effort is underway while initiate streamline healthcare data exchange because the current architecture is based on centralized system, which is not feasible to handle and transmit transactions in a distributed environment [36, 37]. Blockchain is the only solution that enhance interoperable effectiveness and provide improve integration between nodes. In addition, it provides a greater consistency in terms of platform standardization and related protocols, which directly effects on the design of cost-efficiency, where patient cannot be retested. However, the involvement of regulatory and compliances by the government, the ecosystem perform actively in technological supplies, quality assessment, experience, and monitoring. Furthermore, this blockchain-enabled solution not only answers the seamless data exchange problem but also addressed a list improvement in healthcare domain, which is highlighted as follows:

  • Digital transformation-frictionless secure data sharing can be leverage.

  • Platform interdependency-provide ease workforce data management.

  • Data exchange-provide framework for share data in a standard manner.

  • International border law-provide interoperability between explicit node interconnectivity within easier and more efficient manner.

5.2 Fine-grained stakeholder authentication and privacy

Recently, different methods are introduced that addresses secure data access and controls. However, fine-grained is one of them, which not only provide a controlling scenario but ensure certain data accessibility and availability [36, 38]. In healthcare, we compare generalized data access and control scenario with coarse-grained method, where fine-grained perform more sophisticated in terms of following nuanced steps and variable operations for access enrollment purpose. Substantially, this adaptation mainly ensures a list of limitation that are involved in the existing healthcare environment as follows:

  • Multi-data source storage, exchange, and access facility

  • Provide degree of access in accordance with the assigned roles

  • Mobile access and security facility

  • Ensuring third-party accessibility

5.3 Security loopholes and storage cost-effectiveness

A large number of healthcare applications are running on an outdated design, protocols, compliances, or even operating systems, which drastically exacerbating security and privacy challenges. A list of common vulnerability raises in the healthcare environment is mentioned as follows [39,40,41,42]:

  • Cryptographic attacks

  • Cybercrime like malicious insider attacks

  • Denial of service (DoS)

  • Distributed denial of service (DDoS)

  • Injection exploits

  • Malware

  • Web security exploits

  • Privilege escalation

Blockchain Hyperledger technology plays a significant role in addressing these issues by offering an affordable, effective, and adaptable architectural environment for transforming seamless, interoperable healthcare data, or even managing cross-border information exchanges within the designated computing resources.

6 Conclusions

This paper explored the real-time trends in IoMT, wherein advanced digital technology plays a significant role. The goal is to offer a novel approach to the design and development of healthcare apps that are interoperable and directly contribute to the advancements in the healthcare industry. Throughout the investigation process, this study uncovers a few difficult issues, particularly one pertaining to interoperability, that have a significant impact on the present lifetime of IoMT. In order to manage and safeguard the current IoMT functioning, including data processing, organizing, optimizing, resource management, scalability, and data exchange via distributed preservation (such as IPFS—InterPlanetary File Storage System) to ensure automation in decision-making, along with the security and privacy, this paper evaluated all such possibilities and proposed a novel and secure architecture (named BDLT-IoMT). This architecture uses the collaborative technique of blockchain with SVM. In order to protect automated decision-making, the suggested BDLT-IoMT experiences significant changes in infrastructure security, according to simulation data. Furthermore, in order to enable smooth intercommunication among heterogeneous devices, the proposed BDLT-IoMT guarantees integrity between seamless data sharing and exchanging and network resource optimization. Nonetheless, the evaluation findings demonstrate the importance of the work in the following ways: (i) increased network resource consumption by 1.87%, improved infrastructure security by up to 1.37%, and increased integrity of seamless data transfer by up to 1.56%.