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Groundwater management in Danish municipalities relies heavily on decision-support from numerical flow simulations to evaluate the impact of drinking‑water abstraction on the surrounding environment and the risk of contamination. We would require that this decision-support is as informative as possible and should therefore consider uncertainty in model input data. However, most applied groundwater models are deterministic, built on a single geological interpretation and a fixed set of hydraulic parameters. Such models provide only a single outcome drawn from a whole distribution of possible outcomes, blinding decision-makers to potential unforeseen environmental risks. Fully propagating geological and hydrological uncertainty in these models is necessary to explore all possible outcomes, but this comes at the cost of computationally expensive simulations infeasible to perform within everyday administrative workflows.To address this challenge, we present an approach that utilizes artificial neural networks trained on simulated results from a stochastic model ensemble to emulate the computationally heavy numerical models. The approach constructs an ensemble of groundwater models of the same location with stochastic geology and hydrological layer properties. Simulations run in the model ensemble using MODFLOW present the full outcome space as a distribution instead of a single value. We perform forward particle tracking in the ensemble to delineate probabilistic catchment areas of abstraction wells. The probabilistic catchment areas are used as target data for the neural network to learn from along with a selection of input features. Applied to the Egebjerg catchment, Denmark, the neural network produces catchment probabilities with high accuracy compared to MODFLOW while reducing computation time from hours to seconds.The achieved reduction in computation time makes the neural network suitable within a decision-support tool enabling the use of stochastic models in practice and improving the decision-making process of administrative groundwater management.