Nowadays, air pollution has become a major problem in urban areas. The requirement of studying the air quality is an important task to find the pollution level and to take the necessary measures. The air quality can be determined by the concentration of pollutants present in the atmosphere. Air quality index (AQI) is a measuring term whose value determines about the quality of air. In this report, by taking the historical air quality data of the Coimbatore city and implemented the machine learning algorithms for AQI prediction. Hybrid algorithms such as linear regression with three different clustering algorithms such as the K-means clustering technique, expectation maximization clustering, and DBSCAN clustering algorithms are implemented on the data. Initially, data is collected, and it undergoes some preprocessing steps and then the prediction of AQI is implemented. A comparative analysis is done on the proposed hybrid algorithms and linear regression model in terms of their mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) and the results are drawn.