The importance of soil moisture is recognized globally because it controls hydrological processes that are relevant to agriculture and climate studies. Currently, estimation of root zone soil moisture is largely accomplished using physical models, which are based on flow and transport equations. However, with the complexity of the processes operating in the vadose zone as well as their interactions with each other, parameterizing all the relevant processes is quite a challenge. This complexity is further enhanced by spatio-temporal variability in soil and vegetation properties which demand model parameters to be dynamic. Alternatively, purely data-based methods for root zone soil moisture estimation are still limited despite the growing availability of datasets from networks established within the last decade. Currently, these datasets are used largely for calibration and validation of physical models and retrieval methods from satellites. In this study, we explored the utility of Random Forest (RF) as an approach for predicting and forecasting daily root zone soil moisture from selected stations in the Raam and Twente network. We trained a single RF using meteorological datasets, soil type, land cover type, and LAI as predictor variables. The model was also tuned in order to obtain the optimal hyperparameters (mtry and ntree) and number of training samples. A comparison with model simulation results using Hydrus-1D was also performed. Our results show that RF can accurately predict and forecast root zone soil moisture at the study sites based on RMSE of 0.02 – 0.12 m3m-3, in comparison with Hydrus-1D simulations having RMSE of 0.05-0.22 m3m-3. However, poor results were obtained for saturated water conditions. In addition, 5-95% RF prediction intervals become wider at saturated water conditions for some sites, which indicates higher prediction and forecast uncertainties. RF can be used for root zone soil moisture estimation, especially at data poor regions where information on soil hydraulic parameters are sparse or lacking. It can also be used for estimating missing values at gaps in time series datasets.