In this work, a hybrid modeling approach, combining machine learning (ML) and computational thermodynamics, has been applied to predict deformation-induced martensitic transformation (DIMT) and explore the generic and alloy-specific parameters governing DIMT in austenitic steels. The DIMT model was established based on the ensemble ML algorithms and a comprehensive set of physical variables. The developed model is highly generalizable as validated on unseen alloys. The generic governing parameters of DIMT are in good agreement with previous studies in the literature. However, the evaluated alloy-specific governing parameters reveal large differences between grades, e.g., 204 series of austenitic stainless steels has a quite balanced correlation between strain, stress, temperature, and DIMT, while the 301 series has much stronger correlation between stress and DIMT. The findings in the current study emphasize the importance that a general DIMT model for steels should include both stress and strain, as well as other governing parameters, since DIMT can be both stress-assisted and strain-induced transformation, and often the effect of applied mechanical driving force and the formation of new nucleation sites interact. Graphical abstract: (Figure presented.)