Understanding the vast noncoding cancer genome requires cutting-edge, high-resolution, and accessible strategies. Artificial intelligence (AI) is revolutionizing cancer research, enabling advanced models to analyze genome regulation. This review examines illustrative examples of noncoding mutations in cancer, focusing on both key regulatory elements and risk-associated variants that remain poorly understood, and compares key AI models developed over the last decade for identifying functional noncoding variants, predicting gene expression impacts, and uncovering cancer-associated mutations. The discussion of the goals, data requirements, features, and outcomes of the models offers practical insights to help cancer researchers integrate these technologies into their work, regardless of computational expertise.