The level of air pollution in smart cities plays a critical role in the community’s health and quality of life. Thus, air pollution forecasting would be beneficial and would guide citizens in avoiding exposure to dangerous emissions. The air health of a place can be diagnosed by close observation of the AQI (Air Quality Index) of that place. Moreover, the AQI of a place may have some influence on the pollutant concentration of the neighboring places. To address this issue, this work introduces a hybrid deep learning framework that is able to predict the values of a corresponding metric: AQI of smart cities. As a part of this work, two algorithms are proposed. The first one replaces the missing values in the dataset and the second one formulates the influence of the nearby places’ pollutant concentrations on the air quality of a particular place. A deep learning-based forecasting model is also proposed by combining 1D-CNN and Bi-GRU. To test the applicability of the framework, a large-scale experiment is carried out with the real-world dataset collected from New South Wales, Australia. Experimental results validate that the proposed framework provides a stable forecasting result, it confirms that the AQI of a place gets affected by the pollutant concentration of the nearby places and the comparison of forecasting result with the existing state of the art models shows that the proposed model outperforms the other models.