Digital Twin Technologies for Intelligent Monitoring, Predictive Maintenance, and Autonomous Decision Making in Industrial Cyber-Physical Systems: A Comprehensive Systematic Review

Objectives: This study aimed to systematically review the current state of digital twin technologies in industrial cyber-physical systems and evaluate their roles in intelligent monitoring, predictive maintenance, and autonomous decision-making. It also sought to identify major architectural frameworks, enabling technologies, adoption barriers, and emerging directions for cognitive digital twins in industrial environments. Material and Methods: A structured systematic review methodology was applied using peer-reviewed literature published between 2020 and 2025. Searches were conducted across IEEE Xplore, Scopus, Web of Science, ScienceDirect, and SpringerLink using Boolean combinations of terms related to digital twins, industrial cyber-physical systems, intelligent monitoring, predictive maintenance, autonomous decision-making, and Industry 4.0/5.0. Studies were screened through title, abstract, and full-text review using defined inclusion and exclusion criteria, followed by thematic data extraction, quality assessment, and synthesis. Results: The review included 87 publications and identified five major themes: intelligent monitoring, predictive maintenance, autonomous decision-making, security and interoperability challenges, and future directions. Findings showed that digital twins improve operational visibility, reduce downtime, support fault prediction, and enable self-optimizing processes. Reported benefits included 30–50% reductions in unplanned downtime, 20–40% longer equipment life, and fault diagnosis accuracy of 97.3% in one energy case study. Conclusion: Digital twin technologies are becoming foundational for smarter, more resilient, and sustainable industrial systems, but wider adoption still depends on advances in standardization, security, scalability, interoperability, and organizational readiness