Advanced diagnostic techniques in liver cancer detection are requisite due to the increasing trend in chronic liver diseases worldwide. Despite the various options available for traditional diagnostic methods, they often need more precision to identify early-stage cancers. Better outcomes for patients are always possible with prompt identification and treatment of cancer. This paper discusses how deep learning models can be used to improve liver cancer detection by identifying biomarkers in medical images. Four deep learning models were used: VGG19, U-Net, Inception V3, and ResNet50. Accuracy, precision, recall, F1-score, and specificity were used in evaluating the performance. Of the four models, the highest was VGG19 at 96.7%, followed by U-Net at 93.4%, Inception V3 at 90.23%, and ResNet50 at 88.76%. All models predicted key biomarkers, especially with the highest accuracy at 97.56% given by VGG19. The results suggest that deep learning techniques may appropriately be employed for identifying liver cancer biomarkers that may stage and predict early stages. This mainly leads to bettering the prognosis linked with the disease since early-stage identification generally means more effective treatment options. The research demonstrates that deep learning models complement and enhance traditional diagnostics methods with a highly scalable and efficient alternative. Perhaps their successful application in clinical settings will revolutionize diagnostics of liver cancer, give healthcare providers a reliable tool to obtain accurate, real-time diagnosis, and therefore produce better patient outcomes.