Mumbai, India’s financial hub, experiences severe traffic congestion that results in economic losses, fuel wastage, environmental degradation, and commuter stress. With the rapid growth of vehicles and urban density, traditional traffic management approaches are proving inadequate. This study presents the design, implementation, and evaluation of an AI-driven Smart Traffic Prediction and Management System tailored for Mumbai. The system integrates real-time data from IoT sensors, traffic cameras, GPS traces, and crowd sourced mobile data. A hybrid predictive framework combining Long Short-Term Memory (LSTM) networks and XGBoost was deployed to forecast congestion levels across critical road segments with high accuracy. A pilot deployment in the Andheri East–West corridor demonstrated promising results: improved travel time predictions, reduced commuter delays, and optimized traffic signal operations. The findings highlight AI’s transformative potential in alleviating congestion in Indian megacities, while also identifying challenges such as data heterogeneity, monsoon disruptions, and infrastructure limitations.