This study aims to evaluate the effectiveness of machine learning techniques for predicting groundwater fluctuations in arid and semi-arid regions using data from the Gravity Recovery and Climate Experiment satellite mission. The primary objective is to develop accurate predictive models for groundwater level changes by leveraging the unique capabilities of GRACE satellite data in conjunction with advanced machine learning algorithms. Three widely-used machine learning models, namely DT, SVM and RF, were employed to analyze and model the relationship between GRACE satellite data and groundwater fluctuations in South Khorasan Province, Iran. The study utilized 151 months of GRACE data spanning from 2002 to 2017, which were correlated with piezometer well data available in the study area. The JPL model was selected based on its strong correlation (R² = 0.9368) with the observed data. The machine learning models were trained and validated using a 70/30 split of the data, and their performance was evaluated using various statistical metrics, including RMSE, R² and NSE. The results demonstrated the suitability of machine learning approaches for modeling groundwater fluctuations using GRACE satellite data. The DT model exhibited the best performance during the calibration stage, with an R² value of 0.95, RMSE of 0.655, and NSE of 0.96. The SVM and RF models achieved R² values of 0.79 and 0.65, and NSE values of 0.86 and 0.71, respectively. For the prediction stage, the DT model maintained its high efficiency, with an RMSE of 1.48, R² of 0.87, and NSE of 0.90, indicating its robustness in predicting future groundwater fluctuations using GRACE data. The study highlights the potential of machine learning techniques, particularly Decision Trees, in conjunction with GRACE satellite data, for accurate prediction and monitoring of groundwater fluctuations in arid and semi-arid regions. The findings demonstrate the effectiveness of the DT model in capturing the complex relationships between GRACE data and groundwater dynamics, providing reliable predictions and insights for sustainable groundwater management strategies.