Particulate matter (PM2.5) pollutants are a significant health issue with impacts on human health; however, monitoring of PM2.5 is very limited in developing countries. Satellite remote sensing can expand spatial coverage, potentially enhancing our ability in a specific area for estimating PM2.5; however, some have reported poor predictive performance. An innovative combination of MODIS AOD was developed to fulfill all missing aerosol optical depth (AOD) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS). Therefore, hourly PM2.5 concentrations were obtained in Northeastern Thailand. A Linear mixed-effects (LME) model was used to predict location-specific hourly PM2.5 levels. Hourly PM2.5 concentrations measured at 20 PM2.5 monitoring sites and 10- fold cross-validation were addressed for model validation. The observed and predicted concentrations suggested that LME obtained from MODIS AOD data and other factors are a potentially useful predictor of hourly PM2.5 concentrations (R2 >0.70), providing more detailed spatial information for local scales studies. Interestingly, PM2.5 along the Mekong River area was observed higher than in the plain area. The finding can infer that the monsoon wind brings polluted air into the province from sources outside the region. The results will be helpful to analyze air pollution-related health studies.