Precision oncology has emerged as a transformative paradigm aimed at tailoring cancer diagnosis and treatment based on individual patient characteristics. Traditional diagnostic workflows rely heavily on radiology and pathology as complementary yet often siloed disciplines. Radiology provides macroscopic insights into tumor morphology and spatial heterogeneity, whereas pathology offers microscopic and molecular-level characterization. The advent of artificial intelligence (AI), particularly deep learning, has enabled the integration of these heterogeneous data sources, giving rise to multimodal AI frameworks. This narrative review explores the current landscape of multimodal AI integration of radiology and pathology in precision oncology. We discuss key technological advances, including radiomics, pathomics, and multimodal fusion strategies such as early, intermediate, and late fusion models. Clinical applications across major cancer types—including lung, breast, brain, and gastrointestinal malignancies—are examined, highlighting improvements in diagnostic accuracy, prognostic modeling, and treatment response prediction. Despite promising developments, significant challenges remain, including data heterogeneity, limited annotated datasets, model interpretability, and regulatory concerns. Emerging directions such as explainable AI, foundation models, and large-scale multicenter validation studies are also discussed. Multimodal AI holds substantial potential to revolutionize cancer care by enabling more precise, data-driven decision-making, but its successful clinical translation requires overcoming technical, ethical, and infrastructural barriers.