Degenerative diseases impose a growing global health burden, whereas currently available therapies remain limited by adverse effects, drug resistance, and variable clinical efficacy. This critical narrative review used a structured, database-informed search of PubMed, Scopus, and ScienceDirect to identify original studies integrating metabolomics-based chemical profiling, chemometric analysis, and disease-relevant bioactivity assessment. Searches were conducted in January 2026 without publication-date restrictions. A total of 416 records were identified during the literature-identification process, of which 19 studies were retained for critical narrative synthesis. Across neurodegenerative, cardiovascular, diabetes-related, cancer-related, and hepatic conditions, LC-MS- and GC-MS-based metabolomics were used to differentiate samples with varying bioactivity levels, integrate chemical profiles with bioactivity data, and prioritize candidate features associated with biological responses. Alkaloids, triterpene saponins, phenolics, flavonoids, and essential-oil constituents were identified as putative bioactivity-related markers, with selected candidates receiving additional in vitro , ex vivo , or in silico support. Among the included studies, cardiovascular investigations demonstrated the strongest methodological rigor, particularly through the use of quality-control samples, reported chemometric validation, quantitative confirmation, and multilevel bioactivity testing. In contrast, studies in neurodegenerative, diabetic-related, cancer-related, and hepatic contexts were frequently limited by incomplete quality-control reporting, insufficient chemometric-validation information, uncertain risks of model overfitting, and limited functional confirmation of candidate markers. Overall, metabolomics provides a valuable decision-support framework for natural-product-based drug discovery. Its translational value, however, depends on standardized experimental design, robust quality-control procedures, transparent chemometric validation, biologically guided variable selection, and layered confirmation of candidate bioactivity-related markers.