Abstract: By referring to GF-1 satellite images and taking Merra-2 meteorological data into account as the variables of auxiliary prediction, PM_2.5 prediction model based on ResNet50 network was constructed for the Yangtze River Delta. According to this model, meteorological data could be used to provide an accurate PM_2.5 concentration benchmark, while GF-1 images could be used to predict the spatial change of PM_2.5 concentration in a more reasonable and accurate way. The results showed that the Pearson correlation coefficient of the model was 0.948 and the RMSE was 6.6 μg/m³. As obtained from model inversion, the distribution of 500m resolution PM_2.5 concentration map was verified to be reasonable and robust, indicating that GF-1 remote sensing image and ResNet50 network were suitable for the prediction of PM_2.5 concentration. The model could be used as auxiliary monitoring means to provide data support for PM_2.5 hot spot identification and follow-up epidemiological research in the Yangtze River Delta region.