Computational biology has emerged as an indispensable discipline for deciphering the molecular underpinnings of human disease. By integrating methods from bioinformatics, systems biology, structural analysis, and machine learning, researchers can now model complex biological networks, predict pathogenic variant effects, and identify therapeutic targets at a scale and resolution unattainable by experimental approaches alone. This article provides a comprehensive review of core computational biology methodologies—including molecular dynamics simulation, genome-wide association analysis, network-based approaches, multi-omics integration, and deep learning—and examines their application to elucidating molecular disease mechanisms across oncology, neurodegeneration, metabolic disorders, and infectious disease. We present comparative analyses of method capabilities and documented clinical outcomes, highlighting key advances and persistent challenges. We conclude by assessing the trajectory toward AI-driven, multi-scale disease modelling as the foundation of next-generation precision medicine.