Air pollution has become a major environmental threat, yet traditional monitoring systems rely on expensive fixed stations with limited coverage and delayed reporting. This project introduces an AI-driven Smart Air Quality Monitoring and Predictive Pollution Control System that integrates IoT sensors, edge computing, machine learning, and cloud analytics for real-time, scalable monitoring. A network of low-cost sensors measures pollutants such as PM2.5, PM10, CO₂, CO, and NO₂, sending data to an edge device (ESP32/Raspberry Pi) for cleaning, filtering, and anomaly detection. Edge processing minimizes latency, saves bandwidth, and enables rapid local decision-making. Cleaned data is then uploaded to the cloud, where models like Random Forest, XGBoost, and LSTM generate short- and long-term pollution forecasts. An interactive dashboard visualizes real-time AQI, spatial patterns, and predictive insights to support timely interventions. Overall, this cost-effective system demonstrates key CS engineering skills and offers a practical framework for smarter, healthier, and more resilient cities.