Semiconductor manufacturing relies heavily on automatic test equipment (ATE), and yet thermal aging poses a critical risk to equipment reliability. This study proposes a novel anomaly detection framework for ATE driver boards by integrating cumulative degradation time (CDT)—derived from accelerated life testing (ALT)—with artificial intelligence models. Specifically, the approach quantifies the cumulative effects of thermal stress as CDT and utilizes it as a key input feature to enable the early detection of degradation under prolonged high-temperature conditions. The proposed framework successfully demonstrates the capability to diagnose real-time anomalies before critical CDT thresholds are reached. Consequently, this approach allows for efficient management, significantly contributing to reduced maintenance costs, minimized downtime, and enhanced equipment reliability, serving as a foundational strategy for condition-based maintenance (CBM) strategies in semiconductor manufacturing.