The future of predictive modeling in oncology is bright and will be transformative in the early detection and assessment of cancer risk. This paper applies generative Artificial intelligence (AI) models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which analyze complex and high-dimensional data sets to find subtle risk factors and emergent disease patterns. The advanced integration of data preprocessing, model training, and evaluation techniques with synthetic data generation and clustering algorithms increases the predictive accuracy of our methodology. Some of the proposed models in this work obtained an accuracy of 89%, sensitivity of 85%, specificity of 91%, and an AUC-ROC score of 0.93, considerably outperforming traditional approaches. Key findings involve identifying new risk factors across genetic, environmental, and lifestyle dimensions and demographic-specific trends in cancer susceptibility. Although the study evidences the power of generative AI for personalized cancer prevention and early diagnosis, it also underlines crucial challenges concerning model interpretability, ethical issues, and data privacy. These results give evidence of the huge potential of generative AI in redefining predictive cancer modeling and have obvious translational implications for improving outcomes in individual patients and for strategies in public health.