This study evaluates traditional and deep machine learning (DML) algorithms for predicting groundwater level (GWL) in Izeh City, Khuzestan province, Iran, using three key input variables: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P). The algorithms tested include convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN). The CNN algorithm demonstrated the highest accuracy, with an RMSE of 0.0558 and an R² of 0.9948, and showed robustness against noise and variability. Spearman and Pearson correlation analyses revealed that P and E were the most influential variables on GWL. The study highlights the importance of accurate GWL prediction for water resource management in the region and suggests that the CNN approach can be applied globally to predict GWL in areas facing water scarcity. The research also identifies limitations, including the lack of additional data on water diversion, evaporation, and temperature, and recommends incorporating more input variables to improve prediction accuracy. The study concludes that CNN is a powerful DML algorithm for GWL prediction, offering high accuracy and robustness, and can aid in water resource conservation and crop productivity in Izeh City.This study evaluates traditional and deep machine learning (DML) algorithms for predicting groundwater level (GWL) in Izeh City, Khuzestan province, Iran, using three key input variables: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P). The algorithms tested include convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random forest (RF), and generative adversarial network (GAN). The CNN algorithm demonstrated the highest accuracy, with an RMSE of 0.0558 and an R² of 0.9948, and showed robustness against noise and variability. Spearman and Pearson correlation analyses revealed that P and E were the most influential variables on GWL. The study highlights the importance of accurate GWL prediction for water resource management in the region and suggests that the CNN approach can be applied globally to predict GWL in areas facing water scarcity. The research also identifies limitations, including the lack of additional data on water diversion, evaporation, and temperature, and recommends incorporating more input variables to improve prediction accuracy. The study concludes that CNN is a powerful DML algorithm for GWL prediction, offering high accuracy and robustness, and can aid in water resource conservation and crop productivity in Izeh City.