The large amount of missing values has challenged the application of satellite-retrieved aerosol optical depth (AOD) in mapping surface PM_2.5 concentrations. In this study, we developed a non-AOD random forest model to estimate daily concentrations of PM_2.5 in Guangdong Province, China, where more than 80% of AOD data were missing. The predictive ability of the non-AOD model was compared with that of a AOD-based model. Daily ground-based measurements of PM_2.5 were obtained from 148 ground-monitoring sites in Guangdong with a 60 km rectangle buffer from January 2016 to December 2018. Daily MODIS MAIAC AOD were downloaded from NASA at a resolution of approximately 1 km. Two random forest models were developed to predict ground-level PM_2.5 with one included AOD as a predictor and the other one without AOD. The two random forest models were developed using the same dataset and their predictive abilities were compared. The results of 10-fold cross validation (CV) showed that the non-AOD and AOD-based random forest models yielded similar performance. The CV R² (RMSE) for the non-AOD model in 2016–2018 were 0.82 (8.4 μg/m³), 0.81 (9.5 μg/m³) and 0.78 (9.4 μg/m³), and those for AOD-based model were 0.83 (8.2 μg/m³), 0.82 (9.2 μg/m³) and 0.80 (9.0 μg/m³), respectively. Higher consistency of estimated PM_2.5 were observed in areas close to monitoring sites than those far away from sites, and in southern coastal than northern areas. As the non-AOD random forest model is not affected by AOD missingness, it can be used for epidemiological studies to estimate individual-level exposure to PM_2.5 at a high resolution without spatial or temporal gaps.