Viral hepatitis, encompassing primarily Hepatitis B (HBV) and Hepatitis C (HCV), remains a critical global health burden, contributing significantly to cirrhosis and hepatocellular carcinoma (HCC). Traditional diagnostic modalities, including liver biopsy and standard serological panels, often present limitations regarding invasiveness, cost, and early-stage sensitivity. The advent of Machine Learning (ML) and Artificial Intelligence (AI) has introduced paradigm-shifting methodologies for hepatology [1-5]. This comprehensive review synthesizes the contemporary landscape of ML applications in the screening, diagnosing, staging, and therapeutic management of hepatitis [6-15]. We systematically analyze the efficacy of supervised learning models—such as Support Vector Machines (SVM), Random Forests (RF), and advanced Deep Learning Neural Networks (DNN)—in interpreting complex clinical, genomic, and radiomic datasets [16-25]. Furthermore, we investigate AI's capacity to predict antiviral treatment responses, specifically targeting Direct-Acting Antivirals (DAAs) in HCV [26-35]. By addressing the critical challenges of algorithm interpretability, data heterogeneity, and ethical implementation, this article delineates a translational roadmap for integrating predictive ML frameworks into routine clinical hepatology workflows.

Keywords: Machine Learning; Viral Hepatitis; Hepatocellular Carcinoma; Predictive Analytics; Precision Medicine; Non-invasive Diagnostics.