Health is increasingly recognized as a dynamic state of physiological equilibrium rather than the mere absence of disease. Traditional clinical biomarkers capture only limited aspects of physiology and fail to reflect the multidimensional and dynamic nature of human homeostasis. Metabolomics, by comprehensively profiling small-molecule metabolites downstream of genetic, proteomic, environmental, and lifestyle influences, offers a sensitive and functional readout of an individual’s physiological state. This review catalogues current advances in applying metabolomics to characterize metabolic features of health, focusing on the influence of age, sex, body mass index, physical activity, diet, lifestyle behaviors, microbiome composition, and population heterogeneity. Numerous cohort studies have shown that substantial metabolic variability exists amongst individuals within apparent healthy populations, underscoring the need for stratified and contextual reference frameworks. We further discuss major challenges in defining a standardized metabolic baseline, including analytical platform heterogeneity, biofluid specificity, population diversity, and the predominance of cross-sectional study designs. Finally, we highlight the role of large-scale longitudinal cohorts, biobanks, multi-omics integration, and artificial intelligence–driven tools in overcoming these barriers. Establishing robust, dynamic, and personalized metabolic baselines will be critical for redefining health, enabling early intervention, and supporting predictive and preventive medicine.