In further studies, Machine learning (ML) was employed to measure the significance of various chemical, physical, and structural characteristics on the movement of ions within a crystalline lattice. This work focused on the anti-perovskite crystal structure as a model system. Multiple ML algorithms were trained to predict ion migration barriers, using a table features included anion polarizability (22% for vacancy migration) and defect formation energy (35% for interstitial migration). The discovery of the defect formation energy as a significant feature for interstitial migration is noteworthy as it reflects a kind of Brønsted-Evans-Polanyi-type relation between activation energies and site preference of the charge carriers. Overall, these analyses will aid in designing efficient electrodes and solid electrolytes by narrowing down the essential properties in a multidimensional design space. However, to extend the results beyond anti-perovskites, further research is necessary, such as incorporating dynamic phenomena like lattice vibrations and poly-anion rotations into the feature set.