The human microbiome, a diverse community of microorganisms residing on and within the body, significantly impacts human health. Comprising bacteria, viruses, fungi, and archaea, it influences various bodily functions and behaviors. Understanding its composition and dynamics is a recent focal point. This chapter explores the vital role of computational biology in this understanding. High-throughput sequencing has revolutionized microbiome study, providing detailed profiles. Computational tools handle the resulting vast data. Metagenomic, metatranscriptomic, and metaproteomic techniques, powered by complex algorithms, offer unprecedented insights into microbial populations. Computational modeling, employing techniques like agent-based models and network analyses, simulates microbiome-host interactions, revealing key players in health and disease. Integrating multiomics data uncovers molecular mechanisms governing this relationship, offering potential for targeted therapies. Comparative metagenomics exposes variability in microbial composition across populations, influenced by genetics, lifestyle, diet, and environment. Machine learning aids in identifying health-related microbial markers, enabling precise diagnostics and personalized dietary interventions. In summary, the human microbiome profoundly impacts health. Computational biology, through advanced profiling, modeling, multiomics integration, and machine learning, has illuminated its role in physiology and disease. This chapter emphasizes the crucial contributions of computational biology in unlocking the mysteries of the human microbiome, heralding personalized interventions and advancements in healthcare.