Cancer remains a major challenge in modern medicine, and effective treatment requires accurate modeling of its complex processes. Various approaches have been developed to model cancer progression and treatment, including cellular automata, differential equations, and agent-based models. Differential equations are widely used for cancer growth and treatment response, but they often oversimplify biological complexities and face challenges in parameter estimation. Cellular automata help simulate cancer at the cellular level, though they may not fully capture biological mechanisms. Agent-based models, while insightful, demand significant computational resources. Game theory has emerged as a valuable tool for understanding strategic interactions between cancer cells and their environment, offering insights into tumor evolution and treatment resistance. By modeling cancer progression as an evolutionary competition among cell types, game theory-based models can predict cancer dynamics and help design treatment strategies that lead to better patient outcomes. This approach enhances the understanding of cancer progression and offers potential for creating more effective therapies by integrating experimental findings with mathematical modeling.