In this study, we improve estimates of groundwater usage across the Mississippi Alluvial Plain (MAP) in support of an ongoing USGS effort to model the groundwater resources of the region. Previously, the USGS developed a lookup table based on flowmeter data that estimates water use based on average water use for each crop type, for specific regions and precipitation amounts. The latest iteration of this model is known as Aquaculture and Irrigation Water-Use Model (AIWUM) 1.1 and we refer to our method as AIWUM 2.0. Here, we apply gradient boosted trees (GBT) to predict groundwater use across the MAP from 2014-2019. The predictor variables include well locations (latitude and longitude), crop type, precipitation, maximum temperature (the average daily maximum from April - September), total evapotranspiration estimated with MOD16, and surface run-off (TerraClimate). The existing flowmeter data over the Mississippi Delta were randomly split into training (80%) and validation (20%) data. The following parameters in the GBT algorithm were tuned: the number of estimators, learning rate, maximum tree depth, the objective function, and the percentage of training data that are randomly sampled in the training process. We observe very low model overfitting where the training error metrics are R² = 0.58, mean absolute error (MAE) = 0.30 ft, and root mean square error (RMSE) = 0.51 ft, respectively, and the corresponding test metrics are R² = 0.49, MAE = 0.32 ft, and RMSE = 0.51 ft. This is an improvement over AIWUM 1.1, where the corresponding R², MAE, and RMSE were 0.27, 0.40 ft, and 0.67 ft. These water use estimates will result in an improved ability to accurately model groundwater flow in this aquifer, which accounts for roughly 20% of the total groundwater pumping in the United States.