The key contributors to climate change include air pollution and atmospheric radiation. PM \(_{2.5}\) is especially harmful among various pollutants, posing significant risks to human health and the environment. The reality highlights the pressing need for accurate forecasting models to address this challenge effectively. In this study, we proposed a hybrid Pro-1: Proposed 1DCNN-BiLSTM model, where a wavenet architecture combined with two 1DCNN branches feeds into a BiLSTM layer for PM \(_{2.5}\) prediction. Furthermore, introduce an enhanced version, the Proposed 1DCNN-BiLSTM-XGBoost residual correction model, which incorporates XGBoost-based residual correction to optimize prediction accuracy. The proposed models are compared to traditional deep learning models like BiLSTM, CNN, GRU, LSTM, and RNN. The Pro-1 model demonstrates a noteworthy improvement in RMSE compared to the CNN model, recording a value of 79.909 ± 19.760 across the dataset. It achieves a minimum RMSE of 3.463 in the Colombo dataset, while the Pro-2 model has an RMSE of 0.2658 in the same dataset. The Pro-1 model’s maximum RMSE reaches 23.778 in the Ulaanbaatar dataset, whereas Pro-2 peaks at 2.554. Pro-2 consistently outperforms all models, achieving the lowest RMSE, MAE, and MSE across all cities, as demonstrated by Friedman’s post hoc test, which ranked 1st with statistically significant improvements. The model ranking was \(1DCNN-BiLSTM-XGBoost> 1DCNN-BiLSTM> \text {BiLSTM}> \text {GRU}> \text {LSTM}> \text {RNN}> \text {CNN}\) to the RMSE. Further, the Diebold-Mariano test, AIC-BIC test, and Taylor diagrams will be used to validate the proposed models. These results establish the proposed models as the most effective predictive model for PM \(_{2.5}\) forecasting, offering improved accuracy and reliability for air pollution monitoring.
