Figuring out how much air pollution there is now an important part of making cities less affected by its bad effects. Several machine learning methods have been used to predict the quality of the air. In this paper, we used linear regression, random forest regression, decision tree regression, support vector regression, gradient boosting regression, artificial neural networks, SDG regression, and adaptive boosting regression to try to guess the Air Quality Index of CO, NO2, SO2, PM2.5, PM10, and O3. Our country is becoming more developed, and more people are driving cars. This is making the air quality in our big towns worse. Approximately, three million people die every year because of pollution from PM 2.5 and PM 10. The main way to figure out how many people are hurt by air pollution is to look at measurements from ground tracking networks. Even though coverage is growing, there are still places where monitoring is not good enough. Changes in air quality from place to place have a big effect on the weather.