BACKGROUND/AIMS: Inflammatory bowel disease (IBD) has become a global health concern. With the growing evidence of the gut microbiota's role in IBD, studying microbial compositions across ethnic cohorts is essential to identify unique, populationspecific microbial signatures. METHODS: We analyzed stool samples and clinical data from 254 IBD patients (226 ulcerative colitis, 28 Crohn's disease) and 66 controls in northern India using metagenomic shotgun sequencing to assess microbiota diversity, composition, and function. Results were replicated in 436 IBD patients and 903 controls from the Netherlands using identical workflows. Using machine learning, we evaluated the generalizability of Indian IBD signals to the Dutch cohort, and vice versa. RESULTS: Indian IBD patients exhibited reduced bacterial diversity and an abundance of opportunistic pathogens, including Clostridium, Streptococcus, and oral bacteria like Streptococcus oralis and Bifidobacterium dentium. There was a significant loss of energy metabolic pathways and distinct co-occurrence patterns among microbial species. Notably, 39% of these signals replicated in the Dutch cohort. Unique to the Indian cohort were oral pathobionts such as Scardovia, Oribacterium, Actinomyces dentalis, and Klebsiella pneumoniae. Both Indian and Dutch IBD patients shared reduced butyrate producers. Machine-learning diagnostic models trained on the Indian cohort achieved high predictive accuracy (sensitivity 0.84, specificity 0.95) and moderately generalized to the Dutch cohort (sensitivity 0.77, specificity 0.69). CONCLUSIONS: IBD patients across populations exhibit shared and unique microbial signatures, suggesting a role for the oral-gut microbiome axis in IBD. Crossethnic diagnostic models show promise for broader applications in identifying IBD.