In the field of Engineering, Mechanical Innovation and Advanced Technology, unplanned bearing failures in high-speed passenger train bogies pose catastrophic derailment hazards and operational disruptions. This empirical investigation systematically examines Digital Twin Framework for Predictive Maintenance and Degradation Modeling of High-Speed Rail Bogie Bearings through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing physics-informed digital twin models combining multibody dynamic simulations with continuous IoT bearing vibration streams, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the digital twin predicted outer-race spalling defects 120 operating hours prior to critical threshold exceedance with 96% accuracy. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for railway maintenance authorities and high-speed rolling stock manufacturers. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.