Stem Water Potential (SWP) is the standard method for assessing water stress and irrigation scheduling in tree crops. This method is time-consuming and labor-intensive, limiting data collection to only a few trees in the orchard. To find an alternative approach that predicts water stress in every tree in the orchard, we implemented a novel Stochastic Decision Tree (SDT) method, utilizing remote sensing and weather data to predict SWP in almond and pistachio orchards. The input data for our model included various vegetative indices such as NDVI, GNDVI, OSAVI, LCI, and NDRE, as well as local weather parameters, such as temperature ( \(T_a\) ), relative humidity (RH), air pressure (P), Vapour Pressure Deficit (VPD), and the Water Stress Index (WSI). Our results indicate that the SDT model achieves a prediction accuracy of nearly 94%, Outperforming Random Forest (RF), support vector machine (SVM), and the k-nearest neighbor (KNN) algorithms. We investigated various combinations of collected data under different scenarios to improve the impact of sensor-derived data from pistachio and almond orchards and enhance the accuracy of SWP predictions using an SDT model. Our findings suggest that a data-driven model utilizing cost-effectively collected data can predict water stress. The successful development of a universal model enhances the accuracy of SWP predictions. Moreover, its adaptability and effectiveness allow it to be utilized for different orchards, making it highly applicable to real-world agricultural scenarios.