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Stress is associated with the onset of various neurological disorders, such as depression, post-traumatic stress disorder, and anxiety. Although extensively studied, the metabolic changes triggered in response to stress remain unclear. We conducted a descriptive observational study on acute stress responses in university students, combining psychometric, biochemical, and untargeted metabolomic analyses, along with machine learning predictions. In this study, forty participants underwent both relaxation and stress induction through a modified Trier Social Stress Test. Validated psychometric tests confirmed proper induction of both states. Although most biomarkers show significant changes under acute stress state, the machine learning predictive model identified salivary α-amylase and the State-Trait Anxiety Inventory-state (STAI-s) as potential stress markers. Additionally, several metabolic pathways, including steroid hormone biosynthesis, glycerophospholipid metabolism, linoleic acid metabolism, tyrosine metabolism, and aminoacyl-tRNA biosynthesis, presented alterations under acute mental stress. Our findings highlight the impact of acute mental stress on multiple metabolic pathways directly implicated in stress-related disorders. These findings advance the understanding of the adverse effects systematically associated with stress and provide evidence supporting the potential role of salivary α-amylase and STAI-s as stress markers. Yet, they should be regarded as important hypothesis generators. However, further studies are needed for final validation.