This article provides a comprehensive review of computational modeling in the context of the cell cycle, focusing on its applications in guiding cancer treatment strategies. It compares various modeling paradigms, including deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models, highlighting their strengths and limitations. The review emphasizes the importance of these models in understanding cell cycle dynamics, particularly in relation to cell cycle viability, signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms. By applying these models to critical aspects of cancer therapy, such as drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the article underscores the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. The review also discusses the role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for therapeutic strategy development. Additionally, it explores the tumor microenvironment (TME) and its influence on cell cycle dynamics and tumor evolution, as well as computational approaches for optimizing cancer treatment strategies through the modeling of the cell cycle. The article concludes by highlighting the importance of integrating computational models with experimental and clinical data to bridge the gap between theoretical frameworks and empirical observations, ensuring that computational models are accurate and applicable in real-world settings.This article provides a comprehensive review of computational modeling in the context of the cell cycle, focusing on its applications in guiding cancer treatment strategies. It compares various modeling paradigms, including deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models, highlighting their strengths and limitations. The review emphasizes the importance of these models in understanding cell cycle dynamics, particularly in relation to cell cycle viability, signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms. By applying these models to critical aspects of cancer therapy, such as drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the article underscores the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. The review also discusses the role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for therapeutic strategy development. Additionally, it explores the tumor microenvironment (TME) and its influence on cell cycle dynamics and tumor evolution, as well as computational approaches for optimizing cancer treatment strategies through the modeling of the cell cycle. The article concludes by highlighting the importance of integrating computational models with experimental and clinical data to bridge the gap between theoretical frameworks and empirical observations, ensuring that computational models are accurate and applicable in real-world settings.