The escalating adoption of Electric Vehicles (EVs) has brought the limitations of current Charging Station Manage- ment Systems (CSMS) into sharp focus. Existing infrastructure, which typically provides drivers with only a basic binary status such as “Available” or “In Use,” is insufficient for the dynamic and time-sensitive demands of a high-density EV ecosystem. This lack of granular, real-time insight forces drivers to waste valuable time and energy navigating to congested stations, intensifying range anxiety and reducing confidence in electric mobility. Moreover, such passive systems lead to inefficient utilization, creating persistent congestion at certain stations while others remain underused. To address these shortcomings, we propose the Smart Real-Time EV Charging Station Management System, an advanced AI-powered framework designed to transform the charging experience. The system integrates multiple dynamic data streams, including live station status, real-time traffic information, and vehicle-specific parameters such as battery State-of-Charge (SoC) and current location. Using sophisticated Machine Learning (ML) models, it moves beyond binary in- dicators to generate predictive availability forecasts, estimating precisely when a charging point will become free. A multi-factor optimization algorithm then combines predicted waiting time (TWait), real-time travel duration (TTravel), and driver charging needs to provide the most time-efficient station recommendation. By balancing individual user requirements with overall network load, the system enhances convenience, reduces congestion, and increases charging infrastructure efficiency.

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