Hepatocellular carcinoma (HCC) remains a significant global health challenge, with therapeutic efficacy in advanced stages often limited by underlying liver dysfunction and adaptive resistance. In this review, the evolving landscape of molecular targets and combinatorial strategies is critically examined, with a particular focus on the transition from preclinical discovery to clinical application. While traditional molecular heterogeneity is acknowledged, the aim is to elucidate how emerging computational paradigms are redefining target discovery and therapeutic stratification in HCC. The primary purpose is to evaluate the role of Artificial Intelligence (AI) and Machine Learning (ML) as integrative tools for translating high-dimensional multi-omics data into clinically actionable insights for HCC management. Special attention is given to the capacity of AI-driven frameworks to analyze complex datasets derived from genomics, transcriptomics, proteomics, metabolomics, and epigenomics, thereby enabling the identification of novel predictive biomarkers, patient subgroups, and rational drug combinations. By synthesizing recent preclinical and clinical evidence, this review highlights how AI-guided approaches can accelerate biomarker validation and optimize therapeutic decision-making. Furthermore, the convergence of AI with spatial transcriptomics, digital pathology, and single-cell technologies is discussed as a transformative infrastructure for decoding tumor–microenvironment interactions and spatial heterogeneity. These integrative strategies provide unprecedented resolution into tumor evolution, immune landscapes, and resistance mechanisms. Collectively, the evidence reviewed supports the conclusion that AI-enabled, multi-omics–driven approaches are instrumental in advancing HCC treatment toward a new era of adaptive, spatially informed, and precision-based personalized medicine.
Manan et al. (Thu,) studied this question.