As a critical component of sustainable water management, groundwater level prediction plays a vital role in mitigating droughts and ensuring adequate water supply. For decades, groundwater level dynamics have been primarily studied through physics‐based models, solving partial differential equations. However, interest has increased over the past few years in using Machine Learning (ML) approaches, like Deep Learning (DL) techniques, to study groundwater fluctuation dynamics more efficiently. DL models utilize complex algorithms to identify patterns that may be difficult to observe with traditional physics‐based models, specifically where the underlying physics is complex or poorly understood or where the available physical model is too simple. The article provides an overview of the literature published since 2001, encompassing 91 works that employed ML models to investigate groundwater‐related issues. Within this body of literature, 47 articles employed ML for groundwater level (GWL) modeling. Later, this article delves specifically into the latest advancements in DL for modeling GWL, including recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU), and discusses their technical promising performance and advantages. We found that the most used time scale was monthly, which appeared in 18 articles, followed by the daily time scale, which appeared in 13 articles. The authors of the articles used normalization as a feature scaling method in 18 articles, while standardization was used in 3 articles. Python was the predominant programming language used in 18 studies for developing machine learning models, followed by MATLAB, which was used in 5 articles. Most authors divided their data sets into 60–90% for training and 10–40% for testing. Most studies have focused on pure academic research rather than practical industrial applications. Therefore, this article identifies shortcomings in recent literature on DL for GWL studies and suggests addressing these issues to improve practical application in real‐world settings.