BACKGROUND AND AIMS: The relationship between gut microbiota and biological treatment response in inflammatory bowel disease (IBD) remains incompletely understood. We sought to characterize microbial signatures associated with clinical remission and develop a prediction model for clinical remission. METHODS: We analyzed 16 S rRNA gene sequencing data from two independent public cohorts (n = 231) treated with biologics (infliximab: n = 23; adalimumab: n = 22; ustekinumab: n = 186). Microbial diversity and taxonomic compositions were compared between the remission and non-remission groups. Random Forest algorithm was employed to construct a prediction model using differential genera and clinical features, with performance evaluated through cross-validation. The model was further validated in a local cohort (n = 29). RESULTS: Significant differences in alpha and beta diversity were observed between the remission and non-remission groups (p < 0.05). MaAsLin2 analysis identified 25 differentially abundant genera (p < 0.05). Among these, we selected the top 10 genera with highest importance scores (Parabacteroides_B_862066, Agathobaculum, Ruminococcus_E, Sutterella, Clostridium_R_135822, Hominilimicola, Onthenecus, Butyricimonas, Bariatricus, Hominenteromicrobium) to build the Random Forest model, notably all enriched in remission patients. The model demonstrated robust predictive performance for clinical remission (AUC: 0.895), which was further validated in the local cohort (AUC: 0.750). CONCLUSION: There is a relationship between gut microbial signatures and biological treatment outcomes in IBD patients. A predictive model based on gut microbiota composition may help stratify patients for treatment response. Further investigation of microbiome modulation strategies may enhance therapeutic efficacy.