A histrogram-based boosted regression tree (HBRT) method was used to predict the depth to the surficial aquifer water table (in feet) throughout the State of Wisconsin. This method used a combination of discrete groundwater levels from the U.S. Geological Survey National Water Information System, continuous groundwater levels from the National Groundwater Monitoring Network, the State of Wisconsin well-construction database, and NHDPlus version 2.1-derived points. The predicted water table depth utilized the HBRT model available through Scikit-learn in Python version 3.10.10. The HBRT model can predict the surficial water table depth for any latitude and longitude for Wisconsin. A total of 48 predictor variables were used for model development, including basic well characteristics, soil properties, aquifer properties, hydrologic position on the landscape, recharge and evapotranspiration rates, and bedrock characteristics. Model results indicate that the mean surficial water table depth across Wisconsin is 28.3 feet below land surface, with a root mean square error of 7.40 feet for the holdout data to the HBRT model. Aside from the overall HBRT methods contained as part of the Python script, this data release includes a self-contained model directory for recreating the HBRT model published in this data release. The model directory also includes a model object for the HBRT model used to predict the surficial aquifer water table depth (in feet) for the State of Wisconsin. Three separate directories are available within this data release that define the input predictor variables, water levels, and NHD points for the HBRT model. The 'bedrock-overlay' sub-directory contains geospatial data that define the special selection zones used in the depth-to-water well selection (DTW_well_selection_zones.docx). The 'water-levels' sub-directory contains input files for the NHDPlus version 2.1 points, the State of Wisconsin well construction spreadsheets, and water level summary files. The 'python-attributes' sub-directory contains predictor variable rasters and vector data that predict the surficial water table depth for Wisconsin and a Jupyter Notebook used for the attribution and input files for well and NHD points.