Urban areas in many countries face significant concerns over the presence of aerosol particles and their effects on human health. These particles, which range in size from 1 nanometer to 100 micrometers, can easily penetrate organic matter and transport toxic gas compounds and mineral substances such as carbon monoxide, ozone, nitrogen dioxide, and sulfur dioxide. High concentrations of airborne particles pose serious challenges to health, the economy, the environment, and society, and it is crucial to investigate ways to improve the quality of life for people. The main objective of this study is to develop a framework that accurately predicts aerosol and air quality index (AQI) values in advance by estimating missing data and predicting future data. To achieve this, we propose the DAerosol.GAN.NTM framework, which combines a neural Turing machine with a generative adversarial network (GAN) to address the limitations of previous studies. Our framework outperforms previous methods, including DAerosol.NTM (without GAN) and four baseline studies using Multilayer Perceptron (MLP), Deep Neural Networks (DNN), Long-short Term Memory (LSTM), and Deep LSTM (DLSTM) models, in terms of accuracy, precision, root mean square error (RMSE), mean absolute percentage error (MAPE), and the prediction of aerosol pollution surge hours in advance according to the Time Interval Before and After the Aerosol Event (TIBAAE) criterion.