Air pollution has become a major environmental concern, significantly affecting human health, ecosystems, and industrial environments. The hardware system uses sensors such as MQ7 for carbon monoxide (CO), MQ135 for air quality gases, a dust sensor for par ticulate matter, and DHT11 for temperature and humidity. These sensors are connected to an ESP8266 microcontroller, which collects real-time environmental data. On the software side, a dataset is created using collected sensor values and processed using Ma chine Learning techniques. The XG Boost algorithm is employed to predict air quality conditions and classify them as normal or abnormal. The system also integrates with Firebase for real -time data storage and visualization enabling remote monitoring. The system also integrates with Firebase for real – time data storage and visualization, enabling remote monitoring. This combined hardware and intelligent software solution provides a cost-effective, real-time, and automated approach to improving air quality management in homes and industrial environments

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TITLE :A Systematic Review of IoT-based Air Quality Monitoring and AI Technologies (2025) AUTHOR : Antony Garcia LINK :https://link.springer.com/article/10.1007/s1046 2-025-11277-9?utm_source=chatgpt.com TITLE :AI-Driven Smart Air Quality Monitoring and Predictive Pollution Control System Using IoT and Edge Computing (2025)

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