Hepatocellular carcinoma (HCC) remains among the most severe liver diseases, largely due to the difficulty of identifying the tumor at an early stage. Conventional diagnostic practices such as AFP testing, CT, MRI, and liver biopsy often struggle to detect subtle abnormalities, limiting timely intervention. Recent progress in Machine Learning (ML) and Deep Learning (DL) has introduced data-driven approaches capable of analyzing clinical, biochemical, imaging, and temporal information with greater precision. This work examines how ML classifiers and DL architectures—including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)—can support early HCC prediction through automated pattern discovery, optimized feature representation, and integration of heterogeneous patient attributes. Evidence from recent studies indicates that ML/DL frameworks can strengthen diagnostic accuracy, reduce manual interpretation errors, and assist clinicians in identifying at-risk individuals. The study emphasizes the potential of hybrid AI-enabled systems to modernize liver cancer screening and support early medical decision-making.