With the growing shift toward electric mobility, there is an increasing need for intelligent and data-driven planning of EV charging infrastructure. This research proposes an AI-powered framework for forecasting EV charging demand and optimizing station allocation. Using advanced models such as LSTM, Prophet, and XGBoost, the system predicts temporal demand patterns, while clustering algorithms (K-Means, DBSCAN) identify high-demand zones for infrastructure expansion. The solution is deployed through MLOps pipelines for scalable, continuously monitored performance. An interactive dashboard delivers actionable insights to policymakers, operators, and drivers, offering demand forecasts, geospatial visualizations, and smart station recommendations. By combining predictive analytics with sustainable energy goals, the system enhances efficiency, reduces grid strain, and supports responsible, data-driven EV infrastructuredevelopment.