Air pollution has emerged as a critical public health and environmental concern across the globe, necessitating effective forecasting systems to anticipate hazardous air quality conditions. Traditional monitoring systems, while essential, often fall short in providing timely predictions that can aid in preventive action. In this study, we develop a predictive modeling approach leveraging supervised machine learning techniques to forecast Air Quality Index (AQI) based on historical environmental and pollutant data. The proposed system integrates multiple regression-based models, including Linear Regression, Random Forest, and Support Vector Regression (SVR), to analyze and predict AQI with high precision. The dataset, sourced from publicly available urban air quality monitoring records, was subjected to preprocessing steps such as normalization, feature selection, and outlier treatment. Experimental evaluation indicates that the Random Forest model outperforms others, achieving an RMSE of 12.6 and an $R^2$ score of 0.91, demonstrating its robustness in capturing complex pollutant interactions. The results validate the feasibility of deploying machine learning-based forecasting systems for real-time air quality monitoring, offering valuable insights for policymakers, environmental agencies, and urban planners to implement proactive pollution mitigation strategies.

Keywords: Air Quality Prediction, Supervised Machine Learning, Air Quality Index (AQI), Environmental Forecasting, Random Forest Regression, Pollution Monitoring