Abstract: Air pollution has become one of the most critical environmental challenges affecting human health, climate stability, and sustainable development worldwide. Accurate prediction of air pollutant levels is essential for early warning systems, policy planning, and effective environmental management. This study proposes a machine learning-based framework for air pollution prediction using historical air quality and meteorological data. The system integrates data collection from air quality monitoring stations, weather parameters such as temperature, humidity, wind speed, and rainfall, and location-specific information. Data preprocessing techniques including data cleaning, handling missing values, feature selection, and normalization are applied to enhance model performance. The proposed model utilizes advanced machine learning algorithms, particularly ensemble learning techniques such as XGBoost, to predict major pollutant concentrations including PM2.5, PM10, NO₂, SO₂, and CO. The predicted pollutant values are further used to compute the Air Quality Index (AQI) and classify air quality into categories such as Good, Moderate, Poor, Very Poor, and Severe. Experimental evaluation demonstrates that the proposed approach improves prediction accuracy and reduces error compared to traditional statistical models. The developed system also supports real-time alerts, hotspot identification, and decision-making support for environmental authorities. This research contributes to sustainable urban planning and public health protection through intelligent air quality forecasting.

Keywords: Air Pollution Prediction, Machine Learning, XG Boost, Air Quality Index (AQI), PM2.5, PM10, Environmental Monitoring, Data Preprocessing, Ensemble Learning, Real-Time Air Quality Forecasting, Smart Cities, Environmental Sustainability.

Downloads: | DOI: 10.17148/IARJSET.2026.13328