The implementation of electric vehicles (EVs) is decisive, alleviating in environmental impacts related to traditional combustion engine vehicles. Nevertheless, the constrained range and long times of EVs endure remarkable challenges, exacerbated by dependence on gird connected charging infrastructure. Incorporating solar power into EVs presents a promising solution to extend range and reduce reliance on external charging sources. Yet, efficiently harnessing solar energy in dynamic real-world condition remains a technical hurdle. Hence, this article addresses these challenges by proposing a high-gain single-ended primary inductor converter (SEPIC) and an artificial neural network (ANN)-based maximum power point tracking (MPPT) controller for PV energized EVs. The SEPIC converter design focuses on maximizing energy extraction from solar panels under changing ecological circumstances, crucial for EVs. ANN-based MPPT technique, tuned using Butterfly Optimization Algorithm (BOA), enhances the accuracy and efficiency of solar energy harvesting. The MATLAB/Simulink validation demonstrate that the proposed system achieves significant improvements in energy conversion efficiency and overall performance.