In view of the spatial and temporal gaps left by the ground monitoring network for fine particulate matter (PM_2.5), remote sensing has been regarded as an effective monitoring alternative to provide data of the PM_2.5 distribution at a high resolution. Satellite-derived aerosol optical depth (AOD) is a parameter that has a good correlation with the PM_2.5 concentration. In this study, a modified deep learning model was established to estimate hourly PM_2.5 concentrations in the Yangtze River Delta Urban Agglomeration (YRDUA) region based on 5-km AOD data from Himawari-8. The model incorporated a density-based spatial clustering of applications-with-noise (DBSCAN) cluster analysis and a deep neural network (DNN) (denoted as DBSCAN–DNN) to construct individualized DNNs for the retrieval of PM_2.5. The DBSCAN algorithm was used to identify the outlying datasets by involving the relationship between AOD–NO₂ and PM_2.5. The cluster analysis divided the inputs into separated clusters with distinct pollution levels, which helped to build a reference for the construction of individualized DNNs. The results showed that the DBSCAN–DNN model could greatly improve the estimation accuracy of the PM_2.5 concentration based on identical inputs when compared with the pure DNN model. The 10-fold cross-validation R-value was enhanced by over 30%, with the highest R-value reaching 0.94 when applied to Shanghai dataset of 2018. The root-mean-square prediction error (RMSE) was also reduced by over 30%. In addition, the model performed well in generating hourly spatial estimations, thereby showing more detailed information for the dynamic changes of the PM_2.5 concentration during the day. Moreover, according to the comparisons of five regional estimation results, the model was proven to have a good applicability to deal with a differentiated data volume and data complexity. This study not only proposes a new method to achieve PM_2.5 estimations with a higher accuracy and spatiotemporal resolution, but also provides a new perspective for exploiting the sophisticated correlations among large environmental datasets by introducing pre-clustering into the deep learning approach.