Depression, a common mood disorder, has been associated with gut microbiota alterations, though the underlying microbial mechanisms remain unclear. This study investigated potential gut microbiota biomarkers and functional pathways in 106 antidepressant-naïve depressive patients and 151 healthy controls, with careful of confounding factors. Stool samples were analyzed using 16S rRNA sequencing, revealing significantly lower alpha diversity and distinct beta diversity in depressive patients. Eleven taxa with differential abundance were identified, including Dialister and Lactococcus (decreased) and Hungatella, Sellimonas, and Lachnoclostridium (elevated), which may relate to gut inflammation and depressive symptom severity. Functional pathway analysis highlighted 36 altered pathways, including those involved in purine degradation, lipopolysaccharide biosynthesis, and amino acid metabolism. A random forest classification model built using the identified taxa achieved moderate accuracy (~0.72) in distinguishing depressive patients from controls. Additionally, we developed a novel Depression Dysbiosis Index (DDI), which positively correlated with depression severity and effectively differentiated between groups. The DDI was robust across analyses, emphasizing its potential clinical value. Future research should incorporate longitudinal designs, advanced sequencing techniques, and additional clinical factors to deepen our understanding of the gut-brain axis in depression and improve diagnostic and therapeutic strategies.