Electric vehicles (EVs) heavily depend on robust and efficient charging systems, particularly when incorporating Renewable Energy Sources (RES) like photovoltaic (PV) systems. One challenge in such system is optimizing power extracted from PV array under variable environmental circumstances, including fluctuating intensity and temperature. This research presents an advanced method to resolve this issue, introducing Maximum Power Point Tracking (MPPT) method-based Radial Basis Function Neural Network (RBFNN). This paper is tailored for hybrid SEPIC-CUK converter, aiming to enhance efficiency of EV charging. The hybrid SEPIC-CUK converter combines the advantages of both converters to handle a wide input voltage range and proficiently manage energy flow between PV array and EV battery with high voltage gain and minimized ripple currents. The RBFNN-based MPPT controller is introduced to dynamically adjust changes in environmental conditions, thereby enhancing the power extraction process. For analyzing effectiveness of proposed framework, MATLAB/Simulink is executed. Simulation results validate that the RBFNN-based MPPT strategy with hybrid converter significantly improves the voltage gain, tracking accuracy, response time, and overall efficiency of the charging system compared to conventional approaches. Thereby, this approach ensures optimal power utilization and contributes to more reliable and effective EV charging solutions.