The rapid growth of Electric Vehicles (EVs) is reshaping global transportation while simultaneously placing new demands on power grid infrastructure. This study presents a real-time energy storage management framework for electric vehicle fleets to enhance grid integration through an optimized Vehicle-to-Grid (V2G) approach. Motivated by increasing EV adoption projected to reach 145 million units by 2030—and the accelerating development of charging infrastructure in major markets such as China, the USA, and the European Union, this research addresses the challenges of battery cost, charging availability, and grid stability. A modular EV–V2G–BESS architecture is developed and controlled using predictive algorithms based on electricity price and load demand. Validation through MATLAB, Python, OpenDSS, and HOMER Pro demonstrates strong adaptability to grid dynamics, achieving high forecasting accuracy (RMSE < 4%) and fast computation time (<60 s). Simulation results indicate that integrating PV and BESS into the V2G framework can reduce operational costs by up to 18% and lower carbon emissions by approximately 35%. These findings highlight the potential of real-time fleet-level V2G management to deliver technical robustness, economic efficiency, and environmental benefits, supporting long-term sustainable energy strategies.