Cancer incidence remains a significant global health challenge, with over 19 million new cases and 9 million deaths reported in 2022. Compounding this issue are overdiagnosis and overtreatment, which not only impose unnecessary costs on patients but also expose them to potential side effects and complications from treatments that might not have been needed. In this presentation, we will explore research approaches that harness the power of artificial intelligence to transform cancer care. Our work focuses on developing AI-enabled models that predict the risk of recurrence and mortality in patients with various cancer types, including breast, lung, and head and neck cancers. Additionally, our research aims to identify which patients are likely to respond to specific treatments, such as chemotherapy or immunotherapy. This approach leverages computational pathology to extract valuable biomarkers from digitized tissue samples, providing insights that surpass the capabilities of traditional methods. By integrating advanced machine learning algorithms with high-resolution imaging data, we can offer more accurate and personalized prognostic and therapeutic predictions.