Precision oncology has revolutionized cancer care by tailoring treatment strategies based on individual patient characteristics, including genetic makeup. Genomic data analysis plays a pivotal role in this paradigm shift, enabling the identification of complex disease mechanisms and genetic variants that drive tumorigenesis. Leveraging artificial intelligence (AI) techniques, such as machine learning and deep learning, has further enhanced our ability to decipher the intricate relationships between genomic alterations and cancer phenotypes. This paper presents a comprehensive review of the application of AI in genomic data analysis for precision oncology, focusing on its contributions to diagnosis, prognosis, and treatment selection. Firstly, we delve into the understanding of complex disease mechanisms elucidated through genomic data analysis. By identifying key genetic variants and molecular pathways implicated in cancer progression, AI-powered approaches facilitate a deeper comprehension of disease biology. Subsequently, we explore how AI techniques augment diagnosis accuracy and efficiency by integrating diverse genomic data sources and enabling early detection of cancer. Moreover, AI-driven genomic analysis holds promise in improving prognostic predictions, aiding in the identification of biomarkers for disease progression and facilitating real-time monitoring of treatment response. Finally, we discuss the role of AI in guiding treatment selection and personalized medicine, where genomic profiling enables the customization of therapeutic regimens to maximize efficacy and minimize adverse effects.