Background Air pollution is a major environmental risk to health, responsible for one in every nine deaths globally. Currently, limited spatiotemporal air pollution data is available to conduct large-scale epidemiological studies investigating the adverse health effects of air pollution in South Africa. Here we aim to model South Africa’s daily average PM10 concentrations for years 2010 to 2016 using spatial and temporal predictor variables including South Africa’s data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Copernicus Atmosphere Monitoring Service (CAMS). Methods We followed a two-stage modelling strategy. In stage 1, we imputed missing Aerosol Optical Depth (AOD) data, due mainly to cloud cover, by developing Random Forest (RF) models between AOD and CAMS AOD estimates also including additional large-scale spatiotemporal predictors. In stage 2, we developed RF models to explain the measured PM10 concentration using the imputed AOD data from stage 1 and spatiotemporal predictors including land cover, road density, population density, altitude, climatological zones, and meteorological variables. Results Preliminary analysis for 2016 shows that our stage 1 RF models can explain 96 percent in variability in the AOD data. In stage 2, our models explain 79% of the variation in measured ground level PM10 concentrations with the corresponding 10-fold cross-validation R2 of 78%. The RF stage 2 model includes variables depicting some PM10 source related variables like day of the week, geographical coordinates, road density, altitude and meteorological predictor variables in the top 10 of influential predictor variables. Conclusion This is the first study showing the potential of earth observation data to develop models explaining fine scale (1x1 km) temporal (daily) variation of PM10 across South Africa. We developed models for years 2010 to 2016. The predicted PM10 concentrations will be made available to facilitate health research in South Africa.