The accelerating integration of renewable energy resources into modern power systems has increased reliance on power electronic converters for efficient energy conversion, grid synchronization, voltage regulation, and power-quality management. However, converters deployed in photovoltaic, wind, and energy-storage systems operate under variable loading, switching stresses, thermal cycling, environmental disturbances, and component degradation, creating complex fault mechanisms that can compromise reliability and energy availability. Conventional fault-diagnosis approaches based on fixed thresholds and predefined physical models often struggle to distinguish interacting faults and identify their underlying causes under dynamic operating conditions. This study investigates an intelligent framework that applies machine learning techniques to fault diagnosis and root-cause analytics in renewable power converters. Electrical, thermal, operational, and switching data are processed to extract discriminative fault signatures and degradation indicators. Supervised and unsupervised learning techniques are employed to detect anomalies, classify converter faults, and associate observed abnormalities with probable root causes. The framework further integrates feature importance and explainable analytics to improve diagnostic transparency. This approach enables earlier fault detection, reduces diagnostic uncertainty, supports predictive maintenance, and improves converter reliability, operational resilience