Despite the widespread use of metabolomics and proteomics to explore the molecular landscape of depression, there is a lack of consensus regarding dysregulated molecules with replicable evidence. Thus, this study aimed to identify robust metabolomic and proteomic features in depression by integrating evidence from large-scale studies. In this study, a knowledge base-mining approach was adopted to compile a list of dysregulated molecules derived from metabolomic and proteomic studies. A vote-counting approach was performed to identify consistently altered molecules in the blood and urine samples of patients with depression. A total of 2398 molecular entries were selected, comprising 857 unique metabolites and 468 unique proteins from 143 metabolomic and 23 proteomic studies in depression. The results of vote-counting analyses revealed that 11 metabolites in blood and 5 metabolites in urine exhibited consistent disturbances across studies. Circulating levels of glutamic acid and phosphatidylcholine (32:0) were elevated in depressive patients, whereas the levels of tryptophan, kynurenic acid, kynurenine, acetylcarnitine, serotonin, creatinine, inosine, phenylalanine, and valine were lower. Urinary levels of isobutyric acid, alanine, and nicotinic acid were higher, whereas the levels of N-methylnicotinamide and tyrosine were lower. Moreover, analysis of the proteomic dataset identified only one circulating protein, ceruloplasmin, that was consistently dysregulated. Convergence comparison prioritized tryptophan as the top-ranked circulating metabolite, followed by kynurenic acid, acetylcarnitine, creatinine, serotonin, and valine. Collectively, robust evidence of metabolomic changes was observed in patients with depression, pointing to a role as potential biomarkers. Further investigation of consensus proteomic features for depression is necessitated.