Background: Inflammatory bowel disease activity is thought to be linked to gut microbiome disruption, but taxonomic studies often yield inconsistent or weak associations. We investigated whether integrating bacterial, fungal, viral, metabolomic and functional data could better capture disease activity than taxonomy alone. Methods: Longitudinal multi-kingdom microbiome, metabolomic (LC/MS and GC/MS) and dietary/lifestyle data were collected across relapse and remission states. Machine learning models were built to predict current and future disease activity, and analyses were adjusted for 48 confounding lifestyle and medication factors. Multi-omic taxa–pathway–metabolite modules were constructed to identify disease-associated signatures, with Mendelian randomisation used to explore causality. Results: Disease activity explained <1% of bacterial and fungal beta-diversity, with medications and diet as dominant compositional drivers; individual variation exceeded disease-related effects. Viral taxa, unlike bacteria and fungi, were largely elevated during active disease. Machine learning combining bacteria and diet achieved the strongest predictive accuracy (viral data: AUC 0.94 for current activity; bacteria+fungi+diet: AUC 0.89 for future activity), while fungal data performed worst. Metabolites, particularly arachidonic acid, bile acids, polyols and aromatic amino-acid derivatives, distinguished relapse from remission more strongly than taxonomy. Functional signatures implicated bacterial persistence mechanisms (biofilm formation, CAMP resistance), driven mainly by E. coli and K. oxytoca, alongside oxidative stress and altered carbon metabolism. Conclusion: Multi-kingdom, metabolic and interaction-level signals substantially outperform taxonomic shifts in detecting disease activity. These findings support metabolite- and function-based biomarkers as more sensitive indicators of gut inflammation than microbial abundance alone.