We developed a machine learning approach to estimate historical PM2.5 levels in China. It overcame several previous issues related to satellite-based PM2.5 exposure estimates such as data missingness due to cloud cover, model stability across a large spatial domain, and robust hindcasting capability beyond the model fitting period.