The increasing integration of renewable energy systems such as photovoltaic (PV) arrays and wind turbines demands highly efficient and reliable power conversion for motor drive applications. Boost-Luo converters, known for their high voltage gain and improved efficiency, are commonly used in such systems. However, power electronic components and motor drives are susceptible to faults due to thermal, electrical, and mechanical stress, leading to unplanned downtimes and maintenance challenges. This paper presents a comprehensive AI-based predictive maintenance framework for Boost-Luo converter-fed motor drive systems operating in renewable energy environments. The proposed system leverages Deep Neural Networks (DNN) for fault classification and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for Remaining Useful Life (RUL) prediction. Multisensor data including voltage, current, temperature, and vibration are processed through feature extraction and dimensionality reduction techniques. Additionally, a Grey Wolf Optimization (GWO) algorithm is employed to optimally tune the PI controller for voltage regulation and harmonic distortion minimization. Simulation results validate the system’s ability to perform early fault detection, precise RUL estimation, and enhanced converter control, leading to improved system reliability and efficiency.