This paper presents a comprehensive, evidence-based review of Non-Destructive Testing (NDT) as a core enabler of industrial asset integrity and predictive maintenance. It systematically analyses failure mechanisms across four high-criticality sectors—aircraft structures, overhead cranes, high-pressure systems, and bridge closure joints—linking dominant degradation modes such as fatigue cracking, stress corrosion cracking, creep, and shear failure to appropriate inspection strategies. Using validated global datasets from the International Labour Organization (ILO), Bureau of Labor Statistics (BLS), National Board of Boiler and Pressure Vessel Inspectors (NBBI), and Federal Aviation Administration (FAA), the study quantifies the safety, economic, and operational consequences of inadequate inspection practices. A structured comparative framework evaluates key NDT techniques, including ultrasonic testing, phased array ultrasonics, radiography, eddy current testing, magnetic particle inspection, acoustic emission, and emerging GMR-based sensing, across performance metrics such as sensitivity, inspection depth, and automation potential. The work further integrates Risk-Based Inspection (RBI) principles aligned with API RP 580/581 and introduces a five-stage AI-driven diagnostic pipeline combining signal processing, machine learning, and decision-support systems. Finally, the paper examines digital twin–enabled predictive maintenance as a transformative approach for real-time asset monitoring and lifecycle optimization. The study provides a unified framework connecting failure physics, inspection technology, and intelligent maintenance systems for next-generation industrial reliability engineering.