Myosin binding protein-C (MyBP-C) is a sarcomeric protein which regulates the force of contraction in striated muscles. Mutations in the family of genes, including slow skeletal (), fast skeletal () and cardiac (), can result in cardiac and skeletal myopathies. Nonetheless, their evolutionary pattern, pathogenicity and impact on MyBP-C protein structure remain to be elucidated. Therefore, the present study aimed to systematically assess the evolutionarily conserved and epigenetic patterns of family mutations. Leveraging a machine learning (ML) approach, the Genome Aggregation Database (gnomAD) provided variants in , , and genes. This was followed by an analysis with Ensembl's variant effect predictor (VEP), resulting in the identification of 8,618, 3,871, and 3,071 variants in , , and , respectively. Missense variants comprised 61%-66% of total variants in which the third nucleotide positions in the codons were highly altered. Arginine was the most mutated amino acid, important because most disease-causing mutations in MyBP-C proteins are arginine in origin. Domains C5 and C6 of MyBP-C were found to be hotspots for most mutations in the MyBP-C family of proteins. A high percentage of truncated mutations in cMyBP-C cause cardiomyopathies. Arginine and glutamate were the top hits in fMyBP-C and cMyBP-C respectively, and tryptophan and tyrosine were the most common among the three paralogs changing to premature stop codons and causing protein truncations at the carboxyl terminus. A heterogeneous epigenetic pattern was identified among the three MYBP-C paralogs. Overall, it was shown that databases using computational approaches can facilitate diagnosis and drug discovery to treat muscle disorders caused by mutations.