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Forest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery (February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of 132 spectral and texture variables could predict AGB with an R-2 value of 0.94, RMSE of 34.5 Mgha(-1) and % RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11 spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with R-2 = 0.81, RMSE = 36.67 Mg ha(-1) and %RMSE of 19.55%. The results found in the present study demonstrated that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively predict the spatial distribution of forest AGB with adequate accuracy.