Free fatty acids (FFAs), which are considered to be closely related with type 2 diabetes mellitus (T2DM), are not only the main energy source as nutrients, but also signaling molecules in insulin secretion. In this study, gas chromatography-mass spectrometry (GC-MS) coupled with two chemometric resolution methods, heuristic evolving latent projections (HELP) and selective ion analysis (SIA), was successfully applied to investigate plasma FFAs profiling of T2DM. Totally, twenty-three FFAs were identified and quantified. The results showed that HELP and SIA methods could be used to effectively handle overlapping peaks of GC-MS data and hence improve the qualitative and quantitative accuracy. Furthermore, a newly proposed competitive adaptive reweighted sampling (CARS) method coupled with partial least squares linear discriminant analysis (PLS-LDA) was introduced to seek the potential biomarkers. Finally, three fatty acids, oleic acid (OLA C18: 1n-9), a-linolenic acid (ALA C18:3n-3), and eicosapentaenoic acid (EPA C20:5n-3), were identified as the potential biomarkers of T2DM for their powerful discriminant ability of T2DM patients from healthy controls. The study indicated that GC-MS combining with chemometric methods was a useful strategy to analyze metabolites and further screen the potential biomarkers of T2DM.