This data release contains input data used in model development and TIF raster files used to predict the probability of low dissolved oxygen (DO) and high dissolved iron (Fe) in groundwater within the glacial aquifer system in the northern continental United States. Input data include measured DO and Fe concentrations at groundwater wells, and associated predictor variable data. The probability of low DO and high Fe was predicted using boosted regression tree methods using the gbm package in R (v. 4.0.0) in RStudio (v. 1.2.5042). The response variables for individual models were the occurrence of: (1) DO ≤0.5 mg/L, (2) DO ≤2 mg/L, and (3) Fe >100 µg/L. Water-quality data were compiled from three sources, as described in Wilson and others (2019): a compilation of data from numerous agencies and organizations at the state, regional, and local level; the U.S. Geological Survey National Water Information System; and the U.S. Environmental Protection Agency Safe Drinking Water Information System. The resultant datasets consisted of 9,398 DO and 17,422 Fe measurements across the study area. A total of 108 predictor variables were originally considered for model development which included well characteristics, soil properties, aquifer properties, predicted nitrate, hydrologic position on the landscape, and groundwater age. After model refinement, a total of 86, 94, and 40 predictor variables were used for predicting the probability of low DO (0.5 and 2 mg/L) and high Fe, respectively.