<p>Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, remains a major global health concern due to challenges in early detection and limited treatment options. Multi-omics technologies—such as genomics, proteomics, and metabolomics—enable comprehensive insights into the disease’s molecular complexity. This systematic review explores how these approaches contribute to biomarker discovery, molecular classification, and personalized treatment in HCC research. Methods: We conducted a structured review of 32 eligible studies, categorizing their computational methodologies into five primary analytical frameworks: survival analysis, unsupervised clustering, supervised machine learning, differential expression analysis, and pathway/network analysis. Notably, unsupervised clustering and supervised machine learning approaches, such as support vector machines, random forests, and deep learning models, were frequently used for subtype classification, feature selection, and predictive modeling. Results: The review identified that multi-omics approaches are widely used to discover biomarkers, classify HCC subtypes, and predict treatment responses. Common methods include clustering and machine learning. However, clinical validation remains limited, highlighting a gap in translational applicability. Conclusion: From a clinical perspective, multi-omics integration coupled with machine learning holds immense potential for improving early diagnosis, patient stratification, and therapeutic targeting. However, challenges related to data integration, interpretability, and cohort diversity must be addressed to realize this potential. This review underscores the transformative role of machine learning-enhanced multi-omics in reshaping liver cancer diagnosis and treatment and outlines future directions to bridge the gap between computational advances and clinical application.</p>