Understanding the optimal values and interactions of parameters within each process is of highest importance. This study is dedicated to exploring the influence of various parameters and their interactions on ventilation supercavitation phenomena through interpretable machine learning (ML) models. In this study, the characteristics of supercavitation on a disc cavitator with enhanced ventilation at different Froude numbers have been examined through both experimental and numerical means. Subsequently, the data generated from the experimental and numerical methods have been employed to create the optimized ML model. Then to investigate the behavior of important parameters, their interactions with each other, and the resulting impact of these interactions on conditioned cavitation, interpretable machine learning techniques, such as Shapley Additive Explanations (SHAPE), Partial Dependence Plots (PDP), and Individual Conditional Expectations (ICE), were employed within an optimized ML model. The findings highlight that the ventilation coefficient is the most crucial parameter affecting the characteristics of supercavitation. Ventilation coefficient exhibits a non-linear behavior and performs effectively within the range of 0.06-0.12. Additionally, the water speed parameter and the ratio of the back-body’s diameter significantly influence the cavity length and cavitation number. These parameters exhibit complex interactions, shaping the characteristics of blowing supercavitation.

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