Machine learning analysis of 990 participants revealed 13 specific gut bacteria biomarkers that distinguish diabetic kidney disease (DKD) from diabetes alone, chronic kidney disease, and healthy controls with 72-78% accuracy. The algorithm identified novel biomarker types including bacterial presence/absence patterns and hierarchical ratios between different microbial taxa, achieving diagnostic areas under the curve between 0.79-0.86 across disease comparisons. One standout finding centers on Gemmiger species, which showed enhanced carbohydrate metabolism and branched-chain amino acid biosynthesis capabilities in DKD patients. This bacterial signature suggests the gut microbiome actively participates in the metabolic dysfunction driving kidney deterioration in diabetes, rather than simply responding to it. The research advances beyond traditional single-species biomarkers by capturing complex microbial community dynamics through computational approaches. For the millions facing diabetes progression to kidney disease, these bacterial signatures could enable earlier intervention before irreversible damage occurs. However, the cross-sectional design cannot establish whether microbiome changes precede kidney decline or result from it. The work represents a significant step toward personalized microbiome-based diagnostics for diabetic complications, though validation in longitudinal cohorts remains essential before clinical application.