The integration of artificial intelligence (AI) technologies in genomic data analysis has revolutionized the landscape of precision medicine, enabling unprecedented insights into human genetic variation and disease mechanisms. This review examines the current state and transformative potential of AI tools in genomic data processing, interpretation, and clinical application. Machine learning algorithms, deep learning networks, and natural language processing techniques have demonstrated remarkable capabilities in handling the complexity and scale of genomic datasets, from whole-genome sequencing to multi-omics integration. Key applications include variant calling and annotation, polygenic risk scoring, drug target identification, and personalized treatment recommendations. AI-powered platforms have significantly improved accuracy in genetic variant interpretation, reduced analysis time from weeks to hours, and enabled the identification of novel therapeutic targets. Challenges remain in data standardization, algorithm interpretability, and clinical validation. However, emerging AI tools are showing promise in addressing these limitations through explainable AI frameworks and federated learning approaches. The convergence of AI and genomics is accelerating the transition from population-based medicine to truly personalized healthcare, with implications for disease prevention, diagnosis, and treatment optimization.