In the healthcare sector, the advent of artificial intelligence (AI) has led to significant gains in the early identification and detection of dangerous disorders, including liver cancer. The goal of this study is to improve the diagnosis of liver cancer by examining the progressive shift from traditional AI models to sophisticated deep learning algorithmic sequences. Machine learning classifiers and other AI techniques have demonstrated potential in interpreting clinical and imaging data; however, their efficacy is often limited by the intricacy of medical datasets. Complex patterns and features may now be extracted from MRI, CT, and hepatic ultrasound images using deep learning models, specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). These models can limit false positives and achieve high diagnostic precision by utilizing sophisticated feature extraction techniques and extensive annotated datasets. This study illustrates how AI is evolving toward deep learning paradigms to automate liver cancer screening, better clinical decision-making, and improve patient outcomes. These results demonstrate the transformative potential of end-to-end deep learning pipelines in the growth of intelligent, non-invasive liver cancer pinpointing and diagnostic systems.