This study focuses on optimizing resource monitoring and management (RMM) of artificial intelligence (AI) systems. The study begins by discussing the fundamental concepts of Digital Twins and cloud collaboration platforms. Subsequently, a collaborative platform called the distributed Digital Twins cloud is designed, incorporating blockchain technology (BCT) to enhance its security. The utilization of cryptographic algorithms in the BCT ensures data security. The study then presents a comprehensive evaluation of the designed BCT-based distributed Digital Twins cloud. The evaluation results demonstrate that the proposed distributed Digital Twins cloud achieves job scheduling times ranging from approximately 35ms to 40ms during training, with the shortest and longest durations observed. The computed data consistency by the proposed platform ranges from 90% to 98%. In the test set, the proposed model achieves job allocation times ranging from approximately 30ms to 35ms, with the shortest and longest durations observed. The data consistency computed by the platform ranges from approximately 90% to 98%, with the lowest and highest values. The network resource management technology model developed in this study exhibits higher efficiency and intelligence, and its performance is comprehensively evaluated. The research findings indicate that the designed model enhances the data processing capability of the cloud platform and effectively improves the security of platform data management, providing substantial support for the advancement of Digital Twins platforms. Moreover, this study offers technical support for the application, design, and optimization of AI technology in network RMM, providing insights into the efficient application and innovation of AI and network technology for future societal development. It contributes to the profound development of AI technology.