In previous nuclear genomic association studies, Random Forests (RF), one of several up-to-date machine learning methods, has been used successfully to generate evidence of association of genetic polymorphisms with diseases or other phenotypes. Compared with traditional statistical analytic methods, such as chi-square tests or logistic regression models, the RF method has advantages in handling large numbers of predictor variables and examining gene-gene interactions without a specific model. Here, we applied the RF method to find the association between mitochondrial single nucleotide polymorphisms (mtSNPs) and diabetes risk. The results from a chi-square test validated the usage of RF for association studies using mtDNA. Indexes of important variables such as the Gini index and mean decrease in accuracy index performed well compared with chi-square tests in favor of finding mtSNPs associated with a real disease example, type 2 diabetes.