This study proposes a system for the analysis and prediction of the air quality index (AQI) throughout the cities of India. It takes the AQI of different cities in India as an input dataset and analyses the data to understand the patterns to predict the quality of air. Atmospheric pollution contributes to a variety of respiratory issues, including heart disease and lung cancer. Therefore, finding a technique to monitor the air quality index is of the highest importance because it is always preferable to be aware of the degree of pollution as soon as possible so that preventative steps can be implemented. In India, we suggest an analysis and prediction framework for the air quality index (AQI) utilizing support vector machines (SVM) because of their capability to handle high-dimensional data and nonlinear interactions. Overall, this study demonstrates how our machine learning algorithms can be utilized as a tool for precision monitoring and for detecting air pollution index. Early detection of air quality conditions enables us to take necessary action to stop the disease spread and reduce air pollution illness.