In this study, the potential of machine learning (ML) for shallow groundwater level predictions in urban areas is explored. It focuses on curating a training dataset that represents the spatial variability of the water table depth, tests the effect of using different feature variables in ML modeling, and finally, compares two ML models with a physically-based (PB) urban hydrological model. To curate a consistent training dataset, a method of transferring low-frequency groundwater level measurements to a minimum water table depth (MWTD) was developed. Two ML models, one with national maps as feature variables and the other including local high-resolution urban feature variables, were trained against the same 280 groundwater level data points and applied to predict the MWTD at a 10 m spatial resolution for the city of Odense, Denmark. The ML models reached a similar fit to the observations, with an RMSE of 1.1 m and 1.3 m, respectively, and outperformed the urban PB model. In densely urbanized areas, the ML models and the PB model showed up to a 1.5 m difference in predictions of MWTD. The results suggest that ML modeling has the potential to provide spatially high-resolution predictions of the shallow groundwater table in urban areas, which represents a challenge for PB models because of their model structure and the lack of hydrological knowledge hindering meaningful parameterization schemes. Furthermore, a SHapley Additive exPlanation (SHAP) analysis of the feature variables illustrates that ML models can be utilized to explore the hydrological relations in urban domains, by analyzing the feature variables’ relations.