Enzyme substrate specificity involving material conversion processes is pertinent to e.g. biochemistry, metabolic engineering, and environmental remediation; however, it is one of the most challenging assignments in protein engineering. We revealed that the redox cofactor preference of malic enzymes can be strikingly converted by applying phylogenetic analysis to machine learning, without experimental screening. This method can predict mutation positions and candidate amino acids that affect substrate specificity, which is challenging to infer solely from the crystal structures. Machine learning uses the structurally homologous but functionally distinct enzymes’ amino acid sequences as input datasets to efficiently navigate toward the target function, and potentially provide new fundamental insights into enzyme–substrate specificity.