In order to handle both techno-economic and environmental factors, this study presents an innovative energy management algorithm for a hybrid solar and fuel cell-based electric vehicle charging station (EVCS). In order to optimize real-time charging prices and improve renewable energy consumption, the current approach, which is intended for a 20-kW EVCS, makes use of a fuzzy inference system within MATLAB SIMULINK to manage power generation, electric vehicle (EV) power demand, and charging periods. Nevertheless, production instability could be a problem for this strategy. By replacing the fuzzy controller with an artificial neural network (ANN) controller, the suggested approach resolves this problem. Findings show that, in comparison to current flat rate tariffs, the ANN-based algorithm not only offers more steady outputs but also lowers energy costs, making it more affordable to charge for both weekdays and weekends. Furthermore, integrating renewable energy sources with hybrid technology greatly reduces greenhouse gas emissions. Due to the charging station owners' comparatively short payback periods, the idea is both environmentally friendly and financially feasible.