China is becoming one of the most air-polluted countries and is experiencing severe PM_2.5 pollution. To acquire spatially continuous PM_2.5 estimates, numerous statistical methods have been developed through the integration of ground-level measurements and satellite-based observations. The estimation of PM_2.5 concentrations in China is characterized by significant spatial nonstationarity and complex nonlinearity due to the complicated terrain variability and wide geographical scope. Mapping the PM_2.5 distributions across China with high accuracy and reasonable details is still challenging. Superior satellite-based PM_2.5 estimation models need to be developed. Taking advantage of a newly proposed Geographically Neural Network Weighted Regression (GNNWR) model that simultaneously accounts for spatial nonstationarity and complex nonlinearity, we developed a satellite-based GNNWR model to obtain spatially continuous PM_2.5 estimates in China. To comprehensively assess the predictive power of the GNNWR model, the widely used Ordinary Linear Regression (OLR) and Geographically Weighted Regression (GWR) models were also carried out for performance comparison. Experimental results demonstrated that the GNNWR model performed considerably better than the OLR and GWR models in terms of multiple statistical indicators, including coefficient of determination (R ²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Most notably, the fitting accuracy of GNNWR was slightly better than GWR, but its prediction ability was much superior to GWR since the predictive R ²of GWR was significantly improved from 0.683 to 0.831 and the RMSE value was considerably reduced from 9.359 to 6.837. Moreover, the mapped PM_2.5 distributions derived from the GNNWR model presented more reasonable and finer details at a higher accuracy than the other models. Although the spatial trends estimated by GWR and GNNWR models were quite consistent, the estimates of the GNNWR model were more accurate and reasonable since its values were much closer to the ground monitoring observations than those of the GWR model, especially for areas with high PM_2.5 concentrations, such as Hebei Province and southern Shaanxi Province. In addition, thanks to the excellent learning ability of the neural network, the spatial variations in GNNWR estimates were more sophisticated and displayed a richer hierarchical structure of local changes than that of GWR estimates, which better described the varying details of the PM_2.5 across China. In summary, the GNNWR model is a reliable method to effectively estimate PM_2.5 concentrations and can also be used to model various air pollution parameters.