Air pollution is one of the most serious environmental and health concerns in modern urban societies. This study proposes an AI-driven Air Pollution Monitoring and Prediction System that enables continuous observation and accurate forecasting of air quality levels. The system employs IoT-based sensors to gather real-time data on pollutants such as PM2.5, PM10, CO, NO₂, and SO₂, along with meteorological parameters including temperature and humidity. The collected data is transmitted to a cloud environment for preprocessing and analysis. Advanced machine learning and deep learning techniques, particularly time-series models like LSTM and regression algorithms, are utilized to predict future Air Quality Index (AQI) levels. The system also incorpo-rates interactive dashboards and automated alert mechanisms to inform authorities and the public about potential pollution risks. Performance evaluation indicates higher prediction accuracy and lower error margins compared to conventional statistical approaches. The proposed framework supports data-driven environmental management, timely intervention strategies, and sustainable urban development while promoting improved public health outcomes.
Madhumitha et al. (2026) studied this question.