Cancer remains one of the leading causes of mortality globally, posing significant challenges in diagnosis, prognosis, and treatment. Traditional prognostic models often overlook individual patient characteristics, leading to generalized predictions that may not reflect personal risk accurately. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have introduced patient-centric models that leverage personal genetic, clinical, and lifestyle data to enhance prognosis accuracy. These AI-driven approaches provide oncologists with refined insights, enabling personalized treatment strategies that improve survival rates and minimize adverse effects. This research aims to evaluate the accuracy and effectiveness of AI models, explicitly focusing on deep learning and machine learning approaches like Random Forest, in enhancing cancer prognosis. Secondary data from clinical, genomic, and imaging sources (TCGA and TCIA) have been used for model training and validation. Comparative analysis reveals that AI models offer greater precision over conventional statistical methods in predicting cancer stages and outcomes. However, challenges remain in ensuring data accuracy, model interpretability, and addressing privacy and ethical concerns. This study suggests that integrating explainable AI models into clinical practice could support oncologists in tailoring treatments to individual patient profiles, potentially transforming cancer care. Future research should explore further model refinement and integrating comprehensive genomic data to enhance predictive accuracy and clinical applicability.