Abstract

The transition toward smart manufacturing systems under Industry 4.0 paradigms has fundamentally altered the operational logic of industrial production, yet maintenance practices have historically lagged behind automation and process digitization advancements. Corrective and time-based preventive maintenance strategies, while conceptually simple, incur substantial economic penalties through unplanned downtime, redundant interventions, and suboptimal asset utilization. This review examines the engineering and computational foundations of AI-driven predictive maintenance (PdM) as a systems-level solution for smart manufacturing environments. We present a consolidated analysis of core methodological approaches including deep learning architectures for remaining useful life prediction, digital twin integration for virtual asset representation, reinforcement learning for dynamic maintenance scheduling, and industrial IoT-enabled edge-cloud hybrid analytics. Through critical examination of validated industrial deployments—including rotating machinery monitoring, automated production lines, and enterprise-level digital maintenance platforms—we evaluate translational implementation frameworks and their demonstrated operational impacts: downtime reduction exceeding 30%, predictive accuracy surpassing 95%, and quantifiable return on investment in multi-million-dollar ranges. Persistent challenges including data scarcity, model interpretability, legacy system integration, and cybersecurity vulnerabilities are systematically analyzed. Future trajectories emphasize federated learning for privacy-preserving analytics, self-healing cyber-physical architectures, and sustainable manufacturing optimization. This review provides engineering practitioners and computational researchers with an integrated perspective on deploying AI-driven predictive maintenance as a strategic enabler of autonomous, resilient, and economically optimized smart manufacturing ecosystems.