This study evaluates various traditional and deep machine learning (DML) algorithms for predicting groundwater level (GWL) in Izeh City, Iran, using three key input variables: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P). The algorithms evaluated 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 superior performance, achieving the highest accuracy with an RMSE of 0.0558 and an R² of 0.9948. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets, and parallelization capabilities for fast processing. Spearman and Pearson correlation analyses revealed that P and E were the most influential variables on GWL. The research has significant implications for water resource management in Izeh City and the Khuzestan province, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predict GWL in other regions facing water scarcity due to population growth. Future research is encouraged to incorporate additional input variables and further enhance the CNN algorithm's performance.This study evaluates various traditional and deep machine learning (DML) algorithms for predicting groundwater level (GWL) in Izeh City, Iran, using three key input variables: groundwater extraction rate (E), rainfall rate (R), and river flow rate (P). The algorithms evaluated 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 superior performance, achieving the highest accuracy with an RMSE of 0.0558 and an R² of 0.9948. The CNN model exhibited robustness against noise and variability, scalability for handling large datasets, and parallelization capabilities for fast processing. Spearman and Pearson correlation analyses revealed that P and E were the most influential variables on GWL. The research has significant implications for water resource management in Izeh City and the Khuzestan province, aiding in conservation efforts and increasing local crop productivity. The approach can also be applied to predict GWL in other regions facing water scarcity due to population growth. Future research is encouraged to incorporate additional input variables and further enhance the CNN algorithm's performance.