Worldwide, healthcare systems face challenges posed by persistent chronic diseases, which have a long-lasting effect on patient health and well-being. Precisely estimating and predicting the origin and impact of chronic diseases is essential for adequate management, funds allocation, and development of policies related to the disease. Mathematical modeling is an important tool for forecasting chronic disease progression, impact, and course. The current article examines several mathematical models, including compartmental, agent-based, and statistical models, emphasizing their distinctive qualities and uses. The models capture intricate relationships between disease-related variables, environmental effects, patient features, and medical therapies. Mathematical models that use data-driven parameter estimates and validation techniques can simulate disease dynamics, forecast patterns for the future, and highlight important variables. Furthermore, these models aid practitioners and other stakeholders in the medical field in decision-making regarding illness prevention, early detection, treatment plans, and allocation of resources. Importantly, mathematical models enhance initiatives' evaluation process and determine their cost-effectiveness. The models help reveal underlying mechanisms, advance our understanding of chronic diseases, and enlighten evidence-based medical procedures. The development of mathematical modeling opens up new possibilities for diagnosing and treating chronic diseases. However, data quality, model complexity, and model validation are the weak points of mathematical modeling. Therefore, it is important to discuss future paths and the potential influence of mathematical models on changing how the medical community approaches the management of chronic diseases.

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