Myalgic Encephalomyelitis Chronic Fatigue Syndrome (ME/CFS) is a debilitating disease that affects up to 3 million people in in US. Currently, the disease has no known mechanism, treatment, or standard diagnostic. Its most severe symptom is the inability to recover from exercise, strongly impacting the quality of life for patients and necessitating further understanding of the interaction between ME/CFS and exercise. Metabolomics enables the collection of sensitive phenotypic data through measurement and characterization of the small organic molecules that make up the metabolome, providing insights into possible diagnostic markers and expanding on the mechanisms of the chronic disease in response to stimuli. In this study, I utilize metabolomics with the aim of uncovering metabolites of statistical significance between 60 ME/CFS patients and 45 control subjects after rounds of exercise. Patients and control groups were subjected to two 30-minute rounds of strenuous exercise with 24 hours of rest in between. Blood serum samples were collected before and after both rounds of exercise and prepared for analysis by Liquid Chromatography Mass Spectrometry (LC-MS). Data from all four time points were processed to extract spectral features for statistical analysis. Typical metabolomic workflows consider two or more independent groups and are generally not compatible with complex experimental designs where metabolite levels are driven by experimental design interacting effects. To address this limitation, I implemented ANOVA, GLM, GLMM, LMM, GAMM, PCA, and Random Forest to consider all four time points and the contributions of exercise, rest, and gender to each metabolite’s intensity. I found a total of 121 significant metabolites, shared between the female and male cohorts, with an additional 325 significant metabolites from the female cohort and 925 from the male. Comparison between time points showed an increase of significant metabolites after exercise, with a larger increase after exercise on the second day, demonstrating the results of exercise exacerbating the effects of ME/CFS on the metabolome. Further ongoing work aims to identify these significant metabolites from a combination of database matching and manual interpretation of tandem MS spectra collected at relevant timepoints. The results from these statistical analyses uncover novel exercise-related metabolites within CFS that can potentially lead to novel diagnostic markers of ME/CFS and a better-defined disease phenotype.