The decline in groundwater levels in Mashhad Plain, primarily driven by anthropogenic factors, presents a significant challenge to sustainable water resource management. The heavy reliance on these resources necessitates accurate prediction of changes for effective managerial decision-making. This study employs machine learning methods to simulate and predict groundwater level variations in the central part of the plain. Initially, key input variables and optimal time lags were identified using the Partial Autocorrelation Function (PACF) and Frequency Lasso Regression (FLR). Subsequently, the performance of Least Squares Support Vector Regression (LS-SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron Neural Network (MLP), and Radial Basis Function Neural Network (RBF) was evaluated for monthly prediction of groundwater level fluctuations over 30 years (1991-2021). Results demonstrated that all four models simulate groundwater levels with acceptable accuracy. The RBF model exhibited superior performance with R², MSE, NSE, and RMSE values of 1.00, 0.00, 1.00, and 0.009, respectively. The findings affirm the high potential of data-driven models in simulating hydrological processes and can serve as a foundation for developing intelligent tools for groundwater resource management in Mashhad Plain and similar regions, including resource allocation and warning system design.