Recently, Fe-based superconductors have shown promising properties of high critical temperature and high upper critical fields, which are prerequisites for applications in high-field magnets. Critical temperature, T c , is an important characteristic correlated with crystallographic and electronic structures. By doping with foreign ions in the crystal structure, T c can be modified, which however requires significant manpower and resources for materials synthesis and characterizations. In this study, we develop the Gaussian process regression model to predict T c of doped Fe-based superconductors based on structural and topological parameters, including the lattice constants, volume, and bonding parameter topological index H 31 . The model is stable and accurate, contributing to fast T c estimations.