Since most of the applications rely on machine learning algorithms, the customers are affected by the machine-made decisions. This can be resolved by adding more complexity to ML models. Common attacks that are possible to break the reliability of ML techniques are replay attacks, zero-dynamics attacks, and adversarial attacks. These attacks leverage the vulnerabilities of ML and collapse the reliability of data. The development of trusted ML systems can be achieved with the help of technical tools such as probabilistic modeling, Markov decision process. temporal decision process. Then, to achieve a reliable machine-learning framework, a simple way could be to utilize an ensemble for the regular models. This can develop reliable predictions. Another emerging technique is transfer learning that helps to overcome the performance loss and increases the trust score. However, estimating reliability will define the accuracy of predictions, it can work along with transductive reasoning. In general, reliability calculation is defined as the probability of count of true classes from the example set.