Smog triggered due to air pollutants and fog. Deep Learning techniques are applied to predict the smog severity. This paper presents deep learning based predictive model for various air pollutants (NO2, NOx, CO, SO2, O3, PM2.5, PM10) for metropolitan area Air pollution dataset. Central Pollution Control Board (CPCB) is monitoring air, water, waste, etc through nationwide programs. Through National Air Quality Monitoring program, the primary and secondary air pollutants are captured and available in online. In this paper, two traditional predictive models along with deep learning technique Long-Short Term Memory (LSTM) are used for predicting the air pollutants. Before training the model, the missing values and noise in dataset were imputed using mean value. Then, the models are built with LR, ARIMA, and LSTM. Finally, the models performance is measured using Mean Absolute Error and Root Mean Square Error (RMSE). LSTM performed better than LR and ARIMA