Machine Learning Techniques for Enhancing Cyber-Physical Systems: A Comprehensive Review

Cyber-Physical Systems (CPS) represent a foundational paradigm shift in modern engineered systems by integrating computation, control, communication, and physical processes into a unified architecture. As CPS rapidly expand across critical domains such as smart grids, industrial automation, and smart agriculture, the growing complexity, dynamicity, and scale of these environments necessitate the adoption of advanced Machine Learning (ML) techniques capable of enabling autonomous decision-making, predictive intelligence, and resilience under uncertainty. This review presents a comprehensive synthesis of ML methodologies applied to CPS, covering supervised, unsupervised, reinforcement, and deep learning paradigms. The paper further examines their domain-specific applications, architectural integration challenges, security implications, deployment issues across edge–fog–cloud infrastructures, and emerging research opportunities. The analysis highlights the indispensable role of ML in shaping next-generation CPS with improved efficiency, robustness, and adaptability.

Cyber-Physical Systems (CPS), Machine Learning, Smart Grids, Industrial IoT, Industry 4.0, Smart Agriculture, Deep Learning, Reinforcement Learning, Edge Computing

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