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Understanding annual extraction volumes and fluctuations in groundwater depth is essential in water resource management. This study applied various machine learning methods to predict the spatial variability of groundwater depletion (GWD) in an alluvial aquifer located along the southern coasts of the Caspian Sea (Mazandaran Plain). Initially, mean GWD values were measured from 250 piezometric wells across the plain. Subsequently, the factors controlling GWD were identified and provided. These factors encompass natural elements, including precipitation, evaporation, topography, groundwater depth, aquifer transmissivity, and proximity to water bodies. They also include anthropogenic factors such as extraction rates and proximity to industrial centres. Three machine learning models – Random Forest (RF), Deep Learning (DL), and Extreme Gradient Boosting (EGB) – were employed to model GWD using consistent training and test data. In this context, GWD was used as the output, and its controlling factors were incorporated as input variables. Correlation coefficients between GWD and its controlling factors revealed that the transmissivity of aquifer formations (R = 0.81), groundwater withdrawal within well radii (R = 0.69), and groundwater depth (R = 0.6) were the most significant factors. All three models demonstrated high efficacy during the training and testing stages. However, during testing, EGB offered the highest performance in modelling GWD due to extraction (R2 = 0.8, NSE = 0.82). The verified model was then used to predict annual drawdown or extraction across the plain. The predicted results were visually represented as an annual GWD or extraction map within a GIS framework. A comparison between observed GWD values in piezometric wells and the predicted values confirmed the robust performance of the proposed methodology (R2 = 0.8). This methodology can be employed effectively to predict spatial changes in GWD or annual extraction in other plains. [ABSTRACT FROM AUTHOR]