<p>Artificial Intelligence (AI) is revolutionizing computational biology by introducing innovative methods across various fields. This review explores the role of AI in key areas such as drug discovery, genomics, and proteomics. In genomics, AI accelerates tasks like DeoxyriboNucleic Acid (DNA) sequencing and gene function analysis, enhancing our understanding of gene interactions. In proteomics, AI models aid in predicting protein functions, while in drug discovery, AI facilitates virtual testing of drug candidates, target identification, side-effect prediction, drug function analysis, and classification of anatomical therapeutic chemical classes, and drug property optimization, significantly expediting drug development. However, challenges remain, including diverse and noisy datasets, data imbalance, model transparency issues, and the limited generalizability of AI models to new species, cell types, or conditions. The effective application of AI also requires seamless collaboration among computational scientists, biologists, and clinicians, which can be difficult to achieve. This review identifies critical research challenges and proposes solutions, such as improved data integration techniques, more interpretable AI models, and hybrid approaches that combine AI with biological knowledge. Future directions emphasize the need for enhanced data-sharing frameworks, interdisciplinary collaboration, and advanced tools capable of managing complex biological information. By addressing these challenges, AI has the potential to drive significant advancements in computational biology, enriching our understanding of biology and advancing precision medicine.</p>