AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

The exponential proliferation of cancer multi‐omics data offers unprecedented opportunities for precision oncology but presents severe challenges due to high dimensionality, sparsity, and heterogeneity. This article systematically reviews how artificial intelligence (AI) reshapes this landscape. First, we explore generative models (Variational Autoencoders, Generative Adversarial Networks) that reconstruct high‐fidelity data foundations through intelligent imputation and non‐linear batch correction. Second, we dissect the evolution of integration architectures, highlighting Graph Neural Networks for topological feature extraction and Transformers for constructing biological foundation models. Addressing clinical trust, we evaluate Explainable AI (XAI) strategies for transparent biomarker discovery. At the clinical level, we demonstrate AI's superior efficacy in molecular subtyping, survival stratification, and immunotherapy prediction. Finally, we identify meta‐learning, spatial multi‐omics, and federated learning as pivotal directions for realizing the clinical translation of next‐generation precision oncology.