Abstract

Air pollution remains a critical health issue, with traditional monitoring systems failing to provide timely, personalized guidance. This project introduces a Multi-Agent Framework for Real-Time AQI Monitoring and Personalized Health Advice, integrating autonomous agents to collect, process, and act on environmental and health data. The framework uses IoT-enabled sensors, satellite data, and government datasets for high-resolution Air Quality Index (AQI) readings. It leverages user-specific health data (age, respiratory conditions) and employs predictive modeling and machine learning to forecast short-term pollution trends. Unlike conventional systems, this framework delivers proactive, personalized recommendations, such as avoiding outdoor exercise or adjusting routines. The system comprises specialized collaborative agents (Data Collection, Prediction, Health Analysis, Communication). The ultimate objective is to empower individuals with accessible, actionable insights, improving health outcomes and minimizing exposure risks. The framework is highly scalable for deployment in healthcare and urban planning, representing a proactive, user-centric approach to air pollution management.