<p>Cancer is a major cause of mortality and represents a global health concern that affects the life quality of individuals. It is an intricate heterogeneous disease, driven by molecular changes across multiple layers, including genomic, transcriptomic, proteomic, metabolomic, and imaging-derived features. Several large databases, including ICGC (International Cancer Genome Consortium), TCGA (The Cancer Genome Atlas), and others, collect large multi-omics datasets that have greatly contributed to the discovery of cancer drivers, tumor classifications and biomarker identification. However, heterogeneity and dimensional nature of these datasets pose significant analytical challenges for conventional statistical techniques, preventing the full translational potential of precision oncology from being comprehended. Artificial intelligence (AI) and machine learning employ effective computational models that can integrate heterogeneous multi-omics data and identify biologically relevant patterns. The clinical potential of such integrated approaches, for example, AI-based multimodal frameworks that combine imaging and molecular data, has increased prognostic prediction accuracy in breast and lung cancers, as indicated by C-index (Concordance Index, a metric to assess the performance of the model or predictions made by an algorithm) values increasing from ~ 0.61 to 0.75 and from ~ 0.52 to 0.67, respectively. AI-based survival models provide predictive performance up to a 0.91 C-index to predict immunotherapy response in lung cancer. This review article discusses the recent advances in AI-enabled multi-omics integration for cancer research. This review provides an integrated perspective on multi-omics data fusion strategies and further emphasizes the clinical translation of AI-enabled multimodal frameworks, addressing key gaps related to interpretability, data integration, and real-world applicability that are often underexplored in existing literature. It also explores computational methods for multi-omics integration and the clinical application of AI-enabled models for cancer diagnosis, prognosis, and treatment stratification. Furthermore, it outlines the methodological, ethical, and translational challenges that must be addressed before AI-enabled multi-omics models can be applied in the clinical setting. This review altogether aims to elucidate the evolving landscape AI-enabled multi-omics integration in cancer research and precision oncology with the aim to support biomarker discovery, predictive oncology, and data-driven personalized cancer care.</p>