The molecular mechanisms underlying the health-promoting effects of exercise remain to be fully elucidated. As a bridge between genetics, exercise and phenotype, metabolites can be detected in high throughput through metabolomics, offering valuable insights into mechanism elucidation and disease prediction. Metabolic homeostasis is intricately regulated by various factors, including enzyme activity and transporters. Integration of multiple omics technologies such as genomics, transcriptomics, and proteomics enables the comprehensive elucidation of the metabolic network modulated by exercise interventions and facilitates the identification of key metabolic markers. This review summarizes the current research advancements, biological functions, discovery methods, and applications of exercise-induced multi omics metabolic markers, furnishing a theoretical foundation for understanding the mechanisms of exercise-induced health benefits and enabling precision interventions. Relevant literatures from 2000 to 2025 were systematically retrieved from databases including PubMed, CNKI and other databases with the keywords such as “multi-omics”, “metabolic biomarkers”, “exercise”, “health”. Subsequently, the identified literature was meticulously screened to meet the specified criteria and was subsequently incorporated into the study. (1) Exercise induces profound alterations in metabolite levels within the body, with particular emphasis on markers associated with sugar, lipid, and protein metabolism being extensively investigated. As an intensity marker, lactate is implicated in the regulation of fat browning (UCP-1), angiogenesis (VEGF), mitochondrial function (PGC-1α) and metabolic homeostasis (HIF-1α/CES2). Following resistance training, pyruvate levels increase, and an aberrant pyruvate to lactate ratio (approximately 10) may indicate mitochondrial dysfunction. Supplementation with pyruvate has been shown to reduce weight and lipid levels. Ketone bodies regulate metabolism by inhibiting lipolytic enzyme activity and promoting insulin secretion. Plasma ketone body concentrations rise after high-intensity exercise, with levels positively associated with central fatigue. Carnitine levels elevate post-endurance training, and supplementation with carnitine has been linked to increased lean body mass and enhanced cognitive function in older individuals. Serum alanine levels rise following resistance training and, as a precursor of carnosine, supplementation can elevate carnosine concentration by 80%, exerting antioxidant and neuroprotective effects. Creatine, a pivotal molecule in phosphogen energy supply, exhibits a 93% increase in plasma levels post-marathon, with its metabolism intricately related to AMPK activation. (2) Metabolites play a crucial role in disease prediction, particularly in the context of cardiovascular disease where 18 metabolites including glycoprotein acetyl and ketone bodies have been shown to enhance the performance of prediction models. Similarly, in diabetes research, acylcarnitine and other metabolites can improve prediction model efficacy. The combination of multiple metabolites has been found to substantially enhance predictive capabilities for various conditions such as cancer, aging, and other risks, surpassing the predictive power of traditional indicators. (3) Genomics investigations have unveiled the genetic underpinnings of exercise-related metabolites. VO₂max, a significant exercise phenotype with heritability estimates ranging from 0.59 to 0.66, exhibits a negative correlation with the susceptibility to diabetes and cardiovascular disease. SNPs associated with VO₂max, such as variants in the FSHR gene, are positively linked to serum creatinine levels. Reduced creatinine levels have been associated with an elevated risk of T2DM. These findings suggest that creatinine serves as a potential marker of exercise metabolism. (4) Transcriptomic studies have elucidated the molecular mechanisms by which exercise modulates metabolites. Acute exercise induces rapid alterations in the expression profiles of 9 132 transcripts. Exercise elicits upregulation of genes involved in the fructose/mannose metabolic pathway (such as SORD, PFKFB3), suggesting these metabolites may serve as pivotal mediators in the beneficial effects of exercise on Parkinson’s disease. Altitude training enhances the expression of the PHOSPHO1 gene, which encodes an enzyme facilitating choline synthesis. Choline deficiency has been linked to insulin resistance. Choline supplementation has been shown to augment the effects of resistance training, underscoring the significance of choline as a key marker in exercise-mediated metabolic health promotion. (5) Proteomic analyses have unveiled the key mechanisms through which exercise modulates metabolism. Endurance training induces significant alterations in myofibrillar expression, with 237 slow muscles and 172 fast muscles proteins showing differential regulation, of which 65% are associated with metabolism, including ACSL1 and ECHS1. Various training modalities elicit distinct phosphorylation modifications, exemplified by the negative correlation between LDHA3 phosphorylation and lactate levels. Endurance training upregulates SLC25A15 expression in adipose tissue, enhancing arginine synthesis. The post-exercise elevation of plasma GPLD1 levels mimics the neuroprotective effects of exercise on the brain. These findings present novel targets for investigating exercise-related metabolic markers. The application of multi omics technologies has expedited the identification and mechanistic analysis of both established and novel sports-related metabolic markers like lactate. Integrated multi omics strategies (e.g., genome-metabolome) enable the simultaneous examination of metabolic markers and their regulatory mechanisms, facilitating the discovery of exercise-related genetic markers and pivotal regulatory proteins. However, challenges persist, including inadequate data integration and a lack of standardization. Future endeavors should focus on developing dynamic monitoring tools, integrating state-of-the-art approaches such as single-cell/spatial omics, and leveraging AI algorithms for optimized analysis to construct precise predictive models for maximizing health benefits in exercise.