Research investigates the pivotal role of electric vehicles in the modernization of agriculture and the enhancement of sustainability through efficient energy utilization from renewable sources. The study focuses on the development of an uninterrupted hybrid renewable energy source (HRES) integrated with an advanced artificial neural network (ANN) energy management strategy. It specifically examines the optimization of battery charging for agro-electric vehicles (AEV) via a wireless power transfer (WPT) photovoltaic (PV) module located off-board, accompanied by an on-board wind module to energize a Permanent Magnet Synchronous Motor (PMSM) drive. To improve PMSM performance, a modified semi z-source inverter (MSZSI) is precisely designed and analyzed using advanced optimization techniques. The primary objective is to evaluate the effectiveness of an Adaptive Neuro-Fuzzy Inference System (ANFIS) control method in minimizing Total Harmonic Distortion in stator current and torque ripple. Preliminary results demonstrate that the MSZSI effectively boosts voltage from 24 to 48 V. The proposed method achieved an impressive energy utilization rate of 97.35% and a battery storage utilization of 98%. Furthermore, the calculated Total Harmonic Distortion for the PMSM is approximately 10.04%, while the system operates at a rated speed of around 2500 RPM. Overall, the findings suggest that integrating PMSM with ANFIS optimization can significantly enhance the performance of agro-electric vehicle drive systems, advancing effective energy management strategies with a progressive ANN structural system.