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Over the past few decades, rapid industrialization and urbanization have severely deteriorated urban air quality. Forecasting the value of air pollutants and predicting the Air Quality Index (AQI) are crucial in improving the control measures for tackling air pollution. The current research proposes meteorological AQI and pollutants concentration-based AQI predictor. It consists of the pollutant predictor module and Air Quality Index predictor module. In this work, (a) ARIMA, ANN, RFR, XGBoost, LSTM, VAR, and ARMA are used to forecast the concentration of pollutants, (b) support vector regressor (SVR) is then used to build an AQI prediction model using forecasted air pollution concentration (in part ‘a’) and meteorological factors such as wind speed, temperature, and humidity, and (c) the performance of SVR with different kernel functions has been investigated. Also, the impact of considering past AQI values for …