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The 4f2-4f15d1 transition energies of Pr3+ in fluorides are utilized for various optical materials such as solid-state lasers, phosphors, and scintillators. Therefore, it is important to predict such energies of unknown materials for theoretical design of novel optical materials. In this study, we tried to predict the 4f2-4f15d1 transition energies of Pr3+ in fluorides based on first-principles calculations and machine learning. The first-principles calculations were performed based on the relativistic discrete variational multi-electron (DVME) method using the model clusters composed of the central Pr3+ and the anions closer than the nearest cation. Although the calculated 4f2-4f15d1 transition energies of Pr3+ in fluorides showed a relatively good correlation with the experimental ones, the theoretical values tend to be overestimated by ca. 2 eV. In order to improve the accuracy of the prediction, we used the calculated transition energies as an attribute for machine learning. As a result, the regression formula to predict the 4f2-4f15d1 transition energy of Pr3+ in fluorides has been derived by machine learning using the theoretical 4f2-4f15d1 transition energy as well as some other electronic and structural parameters as the attributes. The accuracy of the prediction was significantly improved compared to the simple first-principles calculations.