The issue of air pollution is critical for both the environment and global public health. It is crucial to develop accurate forecasting methods to substantially mitigate the adverse health effects of air pollution. Missing data is common in datasets where specific observations or values are not recorded. To address the problem of missing data in air quality datasets, we used a novel random imputation (nRI) method. This method accurately captures temporal dependencies of air pollution and focuses on continuously missing completely at random (MCAR) and forecasting PM \(_{2.5}\) concentrations. This method accurately captures temporal dependencies of air pollution to focus on continuously MCAR and forecasting PM \(_{2.5}\) concentrations. The Central Pollution Control Board provided the data in this study. Two-step methods for managing missing data follow a specific approach. In the first step, outliers are tackled by replacing them with statistically valid minimum and maximum values determined by the interquartile range (IQR). In the second step, cells that contain NaN (Not a Number) values are filled using random samples drawn from the distribution of the corresponding feature. The proposed (nRI RNN-BiGRU) model outperforms traditional deep learning models in PM \(_{2.5}\) forecasting. It achieves a 27.8792 unit lower RMSE than conventional models and improves the R² score by 0.506. The model also demonstrates significant error reductions across key performance metrics, with a 16.75% decrease in MAE, a 20.07% reduction in MSE, and a 10.03% improvement in MAPE compared to CNN, among others. The experimental results confirm that according to the Friedman ranking, the nRI RNN-BiGRU model consistently ranks as the most optimal model. These findings underscore its effectiveness in air pollution forecasting, supporting proactive environmental protection and public health strategies. Our findings underscore the urgency of the air pollution issue, indicating a likely increase in PM \(_{2.5}\) concentration levels. The potential health risks associated with fine particulates PM \(_{2.5}\) , such as respiratory infections, asthma, and heart disease, further highlight the need for effective strategies for environmental protection and public health. It is, therefore, imperative to take timely, effective measures to address this issue and safeguard public health and well-being.
