<p>Groundwater is a crucial resource for various applications, including domestic, industrial, and ecological needs. Accurate prediction of groundwater level fluctuations is essential for sustainable management, as the groundwater level directly reflects its availability. In recent decades, the use of machine learning techniques for groundwater level modeling has gained significant momentum, driven by advancements in machine learning theory. While existing reviews typically focus on statistical analysis of published papers or evaluate specific machine learning models, a comprehensive and forward-looking review is still lacking. Such a review is essential for both researchers and practitioners in the groundwater field. This review investigates 428 articles published between 1990 and 2023, sourced from the Web of Science. It explores various dimensions of the field, including temporal trends, related disciplines, input variables, machine learning models, and performance metrics. All machine learning approaches, such as supervised learning, unsupervised learning, ensemble learning, and optimization methods, are thoroughly evaluated, and future directions are proposed. The results reveal a significant increase in the number of published articles in recent years, alongside notable progress in interdisciplinary research. Artificial Neural Networks (ANN) and their deep learning variants have become the dominant methods for groundwater level modeling, followed by Support Vector Machines (SVM) and fuzzy logic methods. Additionally, various ensemble strategies, parameter optimization techniques, decomposition algorithms, and unsupervised learning models have been employed to improve model performance. This review highlights key research needs including robust data handling, improved model generalization, standardized workflows, uncertainty quantification, intelligent input selection, and global cooperation for sustainable groundwater management.</p>