The aim of this study was to improve the accuracy and stability of fine particulate matter (PM_2.5) concentration prediction by integrating a deep belief network (DBN) and the kriging interpolation algorithm. First, we utilized a binary particle swarm optimization algorithm to construct the evaluation model, thereby identifying the main factors that most significantly influence the prediction of the PM_2.5 concentration. Second, we thoroughly analyzed the relationship between the real-time PM_2.5 concentration in Xi’an City during different months (or days) and various traditional variograms to obtain training samples for DBN. Finally, on the basis of the selected functions and sample data, we obtained corresponding kriging model interpolation results. The experimental results showed that, compared with the prediction errors of the traditional, spatiotemporal, and empirical kriging interpolation methods, the proposed method yielded a prediction error of only 4.677%. This error was calculated using the Mean Absolute Percentage Error (MAPE), which reflects the average percentage deviation between predicted and actual PM_2.5 concentrations. MAPE was chosen to facilitate comparison with previous studies in air quality prediction. This method increases not only the accuracy of the interpolation algorithm but also its robustness. The core contribution of this study is the introduction of a DBN to optimize the kriging interpolation algorithm, significantly improving the accuracy and stability of PM_2.5 concentration prediction. We combined climate and meteorological data with a deep learning technique to enable the model to adaptively select the most appropriate variogram, thereby more accurately simulating the air quality during different seasons and in various environments. In summary, this study provides a new and effective method for accurate air quality simulation, which has significant implications for advancing meteorological research and improving environmental pollution conditions.