<p>Increasing evidence has highlighted the biological significance of 5-methylcytosine (5mC) DNA modification in regulating tumorigenesis and cancer progression. However, the potential roles of the 5mC modification in HCC are still unknown. A consensus clustering algorithm was performed to determine 5mC modification patterns and establish a 5mC-related gene signature in HCC. Weighted gene co‑expression network analysis (WGCNA) integrated with machine learning approaches (LASSO, RF, and SVM‑RFE) was applied to identify candidate biomarkers, and a 5mC risk score model was subsequently constructed. The concordance index (C‑index), decision curve analysis (DCA), and ROC curve were employed to evaluate the performance of the nomogram. Two distinct 5mC modification patterns were identified, with cluster B exhibiting worse prognosis and enrichment of cell cycle and metabolic pathways. Using integrated WGCNA and machine learning, we constructed a three-gene (DNMT1, SPATS2, MCM6) risk score model that effectively stratified patients into high- and low-risk groups with significantly different overall survival. The risk score demonstrated robust prognostic performance across independent cohorts and was an independent prognostic factor. High risk scores correlated with advanced stage, higher tumor mutational burden, distinct immune microenvironment features, and increased sensitivity to multiple chemotherapeutic and targeted agents. A nomogram incorporating the risk score and clinicopathological features showed acceptable predictive accuracy and clinical net benefit. Experimental validation confirmed upregulation of DNMT1 and SPATS2 in HCC tissues, and their knockdown suppressed HCC cell proliferation, migration, and invasion. The 5mC risk model demonstrates clinical applicability and net benefit in terms of prognostic stratification, immunophenotype characterization, and prediction of drug therapy response in HCC patients, providing a useful tool for precision oncology.</p>