Within the United States’ National Inventory of Dams, 15,000 dams have been classified as having a “high” hazard potential meaning failure or misoperation would lead to probable loss of human life. However, state dam officials evaluate dam hazard potential on a case-by-case basis, ultimately relying on human judgement. Such a process lacks objectivity and consistency across state boundaries and can be time consuming. Here, the authors present a parameterized geospatial and machine learning dam hazard potential classification model to overcome these limitations. The parameters of this model can be tuned for optimal performance. However, for this classification problem, the regulatory and physical implications of the types of model misclassifications are best captured through multiple objectives. Therefore, this research additionally contributes a novel multi-objective approach to machine learning hyperparameter tuning. This research demonstrates the utility of this approach for dams in Massachusetts, United States using a multi-objective evolutionary algorithm to explore different model parameterizations and identify analyst-relevant tradeoffs among objectives describing model performance. Such an approach allows for greater justification of model parameters as well as greater insights into the complexities of the dam hazard potential classification problem.