In the era of Genome-Wide Association Studies (GWAS), biologists have unprecedented access to vast datasets, mirrored in the wealth of information from various omics studies, including genomics, transcriptomics, proteomics, metabolomics, and metagenomics. Integrating diverse data sources has emerged as crucial in unravelling the intricacies of biological processes. This chapter delves into our method for merging various omics methodologies, emphasizing metabolomics and metagenomics data. A powerful strategy addresses data processing challenges and opens new avenues for personalized microbiome-based interventions. The combined analysis of host and microbial metabolomics and metagenomics data has significantly advanced our understanding in diagnosing and treating conditions such as inflammatory bowel disease and irritable bowel syndrome. Metabolic signatures in biological fluids and their microbial counterparts serve as indicators, differentiating health from disease. The sheer volume of data demands sophisticated automated tools for processing and interpretation. Recognizing this need, integrating artificial intelligence (AI) and data science has become increasingly prominent. In this chapter, we combine microbiome and metabolome analyses through publicly available models to elucidate the correlations between microbial and metabolic profiles. By harnessing AI models across various omics data sources, this chapter bridges the gap between data acquisition and clinical applications, paving the way for personalized interventions and optimizing individual health.