Purpose of Review <p>Accurate hepatocellular carcinoma (HCC) risk prediction in patients with chronic hepatitis B (CHB) is essential for optimizing surveillance and treatment strategies. Various risk models have been developed to identify high-risk individuals requiring intensive monitoring, particularly among both untreated and nucleos(t)ide analog (NUC)-treated patients. This review summarizes existing HCC risk prediction models, including traditional and artificial intelligence (AI)-based approaches, while addressing the evolving role of key predictors such as hepatitis B virus (HBV) DNA levels.</p> Recent Findings <p>Conventional models for NUC-treated patients often exclude HBV DNA, assuming its suppression with treatment; however, emerging evidence suggests that baseline HBV DNA may have a long-term, non-linear impact on HCC risk. Despite the growing number of predictive models, their clinical adoption remains limited due to heterogeneity in study populations and the lack of standardized guidelines. AI-driven models have demonstrated potential in enhancing predictive accuracy, yet require further validation for clinical implementation. Additionally, with the advent of functional cure strategies, existing models must be refined to assess HCC risk in post-cure patients.</p> Summary <p>A standardized, integrated risk prediction model applicable to both untreated and treated CHB populations is needed to improve risk stratification and guide personalized surveillance and therapeutic strategies. Future research should focus on incorporating key predictors, validating AI-based models, and adapting risk assessment frameworks for evolving treatment paradigms to optimize long-term outcomes in CHB patients.</p>