Identifying cancer-driver genes presents a major challenge in precision oncology. These genes dictate cancer's growth and spread, crucial for tailoring treatments to individual genetic profiles, known as personalized medicine. Developing accurate models to detect these genes is imperative, as breakthroughs could transform cancer treatment. This project delves into the intersection of machine learning, analytics, and computational biology, carving out a niche for unraveling cancer's genetic intricacies. It introduces an innovative AI toolkit featuring a sophisticated unsupervised model tailored to tackle cancer genetics' complexities.