This review provides a comprehensive overview of computational cell cycle models and their application 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, limitations, and applications. The article discusses how these models can be used to understand cell cycle dynamics, including cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, which are critical for tumor progression and cancer therapy optimization. It emphasizes the potential of computational insights in improving the precision and effectiveness of cancer treatments. The eukaryotic cell cycle consists of four phases: G1, S, G2, and M, each with specific functions in cellular processes. Cell cycle progression is tightly controlled in normal tissues but becomes dysregulated in cancer, leading to uncontrolled proliferation. This dysregulation is due to genetic mutations that impair cell cycle checkpoints and apoptotic responses. Computational models are powerful tools that transform descriptive knowledge into quantitative insights, enabling scientists to explore biological systems without traditional experimentation. These models can decode non-intuitive experimental outcomes and predict various conditions, such as cellular responses to drugs. The review discusses the biological foundation of the cell cycle, including key mechanisms and aberrations leading to cancer. It explores the tumor microenvironment (TME), a complex network of extracellular matrix, diverse cell types, and vascular structures, and its influence on cancer progression and therapy. Computational models in cell cycle research are reviewed, including ordinary differential equation (ODE) models, probabilistic models, first-order partial differential equations (PDEs), Boolean models, and agent-based models (ABMs). These models are compared based on their strengths and limitations, and their potential for synergistic use in understanding cell cycle dynamics. The review also discusses the applications of cell cycle models in understanding biological phenomena, such as the regulation, variability, and interaction of the cell cycle with cellular processes. It highlights the role of computational models in revealing how variability in cell cycle durations affects population dynamics and how control mechanisms introduce significant variabilities to critical aspects of the cell cycle. The review further explores how computational approaches help illustrate the intricate interplay between cell cycle regulation and signaling pathways, shedding light on the mechanisms driving carcinogenesis. Finally, the review discusses computational approaches for optimizing cancer treatment targeting the cell cycle, emphasizing the importance of integrating models with experimental and clinical data to enhance the accuracy and applicability of computational models in cancer therapy.This review provides a comprehensive overview of computational cell cycle models and their application 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, limitations, and applications. The article discusses how these models can be used to understand cell cycle dynamics, including cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, which are critical for tumor progression and cancer therapy optimization. It emphasizes the potential of computational insights in improving the precision and effectiveness of cancer treatments. The eukaryotic cell cycle consists of four phases: G1, S, G2, and M, each with specific functions in cellular processes. Cell cycle progression is tightly controlled in normal tissues but becomes dysregulated in cancer, leading to uncontrolled proliferation. This dysregulation is due to genetic mutations that impair cell cycle checkpoints and apoptotic responses. Computational models are powerful tools that transform descriptive knowledge into quantitative insights, enabling scientists to explore biological systems without traditional experimentation. These models can decode non-intuitive experimental outcomes and predict various conditions, such as cellular responses to drugs. The review discusses the biological foundation of the cell cycle, including key mechanisms and aberrations leading to cancer. It explores the tumor microenvironment (TME), a complex network of extracellular matrix, diverse cell types, and vascular structures, and its influence on cancer progression and therapy. Computational models in cell cycle research are reviewed, including ordinary differential equation (ODE) models, probabilistic models, first-order partial differential equations (PDEs), Boolean models, and agent-based models (ABMs). These models are compared based on their strengths and limitations, and their potential for synergistic use in understanding cell cycle dynamics. The review also discusses the applications of cell cycle models in understanding biological phenomena, such as the regulation, variability, and interaction of the cell cycle with cellular processes. It highlights the role of computational models in revealing how variability in cell cycle durations affects population dynamics and how control mechanisms introduce significant variabilities to critical aspects of the cell cycle. The review further explores how computational approaches help illustrate the intricate interplay between cell cycle regulation and signaling pathways, shedding light on the mechanisms driving carcinogenesis. Finally, the review discusses computational approaches for optimizing cancer treatment targeting the cell cycle, emphasizing the importance of integrating models with experimental and clinical data to enhance the accuracy and applicability of computational models in cancer therapy.