This thesis reflects my deep commitment to addressing the pressing issue of air pollution through innovative technological solutions. By integrating diverse data sources like air quality sensors, weather patterns, and traffic information, I explored how machine learning can be used to predict and manage pollution levels more effectively. Developing this project allowed me to combine my passion for data science with real-world environmental challenges, offering insights into how urban planning and policy can be improved for cleaner air. The forecasting model I created aims to protect vulnerable populations, reduce health risks, and support sustainable urban development. This work is more than a research project; it is my small but meaningful step toward a future with healthier, more sustainable cities. Through this thesis, I hope to inspire actionable change and contribute to preserving our environment for generations to come.