The long exposure to particulate matter (PM) with aerodynamic diameters < 10 µm (PM 10 ) and 2.5 µm (PM 2.5 ) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e. dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e. NO, NH 3 , SO 2 , POA, and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM 10 and PM 2.5 concentrations with a total of 32 parameters for 2015–2016. The results show that the RF-based models produced good performance resulting in R 2 values of 0.78 and 0.73, and RMSEs of 17.08 µg/m 3 and 8.25 µg/m 3 for PM 10 and PM 2.5 , respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., MODIS). The proposed RF models yielded better performance, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and CMAQ).