Molecular biology is undergoing a transformative evolution through the integration of Artificial Intelligence (AI) and bioinformatics, which collectively empower researchers to analyze complex genomic datasets, uncover hidden patterns in genetic information, and advance the paradigm of precision medicine. Notable breakthroughs include AlphaFold’s revolutionary contribution to protein structure prediction, achieving near-experimental accuracy, and PolyPhen’s role in assessing the functional impact of genetic mutations, advancing precision diagnostics. These advancements demonstrate the potential of AI to accelerate discoveries in functional genomics and disease prediction models. However, the integration of these technologies also raises significant ethical concerns. For instance, issues related to genetic privacy have become increasingly critical, as the misuse of sensitive genomic data could lead to discrimination in healthcare and employment. This comprehensive review explores the dynamic intersection of AI and bioinformatics, emphasizing their roles in gene-disease association studies, protein structure prediction, and functional genomics. It also critically addresses challenges, including data quality issues, computational limitations, and the ethical implications of genetic privacy. Future research directions focus on enhancing AI model transparency, overcoming computational barriers, and developing robust ethical frameworks to ensure equitable benefits in clinical and research settings. By integrating cutting-edge AI technologies, such as explainable AI (XAI) and federated learning, with robust bioinformatics methodologies, this review highlights a roadmap for revolutionizing genetic research and fostering advancements in personalized medicine.