Understanding and modeling uplift pressures in hydraulic structures is crucial for structural safety, design optimization, and retrofitting. Despite its importance, research on uplift generated by high-velocity unidirectional flow over offset cracks or joints remains limited. This study establishes accurate uplift models for this particular problem using optimized explainable machine learning techniques, complemented by computational fluid dynamics methods. The models, developed using 558 laboratory experiments, demonstrate high predictive accuracy for both calibration and validation sets. For example, during validation, the models exhibit a mean coefficient of determination (R2) of 0.99, a root mean square error of 0.02, and a mean absolute error of 0.01. The dominant influencing factors for uplift are the gap width-offset height ratio and the relative offset height, which exhibit negative and positive correlations with uplift, respectively. The proposed methodology is also applied to a prototype flood tunnel, yielding satisfactory predictions, with R2 = 0.99 and mean error = 6.5%. This study provides an enhanced uplift modeling approach that ultimately contributes to the resilience and sustainability of hydraulic structures.