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This monograph examines the design and implementation of a smart predictive maintenance platform for CNC machines, integrating real-time sensing, data analytics, and AI-driven diagnostic models. The author explores how hybrid human-machine interaction, anomaly detection, and adaptive decision-making improve equipment reliability, reduce downtime, and enhance overall manufacturing efficiency. The book outlines a practical framework for developing predictive maintenance workflows, supported by case studies and model architectures relevant to modern industrial environments. It offers a comprehensive perspective on building intelligent, resilient, and scalable maintenance systems for next-generation manufacturing.