A Web-Integrated Machine Learning Model for Delhi AQI Prediction to Support Public Health Awareness

Air pollution poses a significant health risk to people living in cities. In Delhi, this city has consistently encountered hazardous air quality levels. This paper introduces a new AI-driven forecast system named the Delhi Air Prediction Web Application which analyses and forecasts the air quality index (AQI) based on machine learning techniques. The system makes use of both historical AQI datasets and actual environmental measurements in real time: such as pm2.5, pm10, no, no2, nox, nh3, co, so2 and other factors like that, to create an accurate prediction of AQI. People can also use the platform's user-friendly web interface for immediate AQI readings in real-time, interactive visualizations of its historical trends, and predictive data. An automatic system module allows daily AQI prediction. In addition, the system includes a rule-based recommendation module that provides health-related suggestions based on AQI levels and user conditions such as asthma, heart issues, and allergies, helping users take appropriate precautions. By merging predictive systems engineering (PSE) with visualization technology plus automatic transfer of information through machine centers, the system can improve awareness about the environment and help anyone living in perennially poisonous urban areas make sound decisions.