Fine particles with an aerodynamic diameter ≤2.5 μm are called PM_2.5, and accurate prediction of PM_2.5 concentration can help prevent the harmful effects of heavy pollution on humans. At present, the distribution of ground-based PM_2.5 monitoring stations in China’s cities is relatively sparse. Hence, the aerosol optical depth (AOD) obtained from satellite remote sensing provides an effective means for large-scale routine PM_2.5 monitoring. In this study, the multi-angle implementation of atmospheric correction AOD (1 km resolution) product from the moderate resolution imaging spectroradiometer (MODIS), meteorological factors, and ground measurements of daily PM_2.5 concentrations from 2016 to 2020 for Dalian were input into a backpropagation neural network (BPNN) to predict PM_2.5 concentrations. To improve the prediction accuracy and stability, a genetic algorithm (GA)-optimized BPNN was further established based on the BPNN to achieve a comparative PM_2.5 concentration prediction. Results showed that the BPNN and GA-BPNN achieved the PM_2.5 concentration by integrating AOD, meteorological factors, and air pollutants with the model test set R² of 0.77 and 0.83 and root mean square error (RMSE) of 11.83 and 9.80 μg m ^{− 3}, respectively. GA-BPNN decreased the SD_RMSE from 0.44 to 0.23 μg m ^{− 3} compared with BPNN and improved the model stability. The spatial distribution of annual averaged estimated PM_2.5 was predicted using GA-BPNN in Dalian from 2016 to 2020. The spatial distribution of PM_2.5 concentrations was generally consistent over 5 years, and the PM_2.5 concentrations exhibited an overall decreasing trend. The BPNN before and after optimization achieved longer-term interannual PM_2.5 daily concentration prediction, and the GA-BPNN had a better prediction effect for extreme values, handled complex fuzzy mapping relationships, and had lower computational complexity. Hence, GA-BPNN was found to be more suitable for practical applications with more advantages for PM_2.5 concentration prediction than BPNN.