This article reviews the dynamics and control of tuberculosis (TB) using mathematical models. It discusses the historical context of TB, its transmission mechanisms, and the development of models to understand and control its spread. The earliest models, developed in the 1960s, focused on prediction and control strategies using simulation approaches. More recent models incorporate dynamical analysis using modern knowledge of dynamical systems. These models address various aspects of TB, including control strategies, optimal vaccination policies, elimination in the U.S., co-infection with HIV/AIDS, drug-resistant TB, immune system responses, demographic impacts, public transportation, and contact patterns. Models use various mathematical tools such as ODEs, PDEs, difference equations, integro-differential equations, Markov chains, and simulations. The article also discusses the impact of different factors on TB dynamics, including the progression of TB, multiple strains, variable latent periods, and exogenous reinfection. It highlights the importance of understanding these dynamics for effective TB control and prevention. The paper also presents theoretical results and bifurcation analysis, showing how TB dynamics can be influenced by various parameters and conditions. The study emphasizes the need for comprehensive models to address the challenges of TB control in different populations and settings.This article reviews the dynamics and control of tuberculosis (TB) using mathematical models. It discusses the historical context of TB, its transmission mechanisms, and the development of models to understand and control its spread. The earliest models, developed in the 1960s, focused on prediction and control strategies using simulation approaches. More recent models incorporate dynamical analysis using modern knowledge of dynamical systems. These models address various aspects of TB, including control strategies, optimal vaccination policies, elimination in the U.S., co-infection with HIV/AIDS, drug-resistant TB, immune system responses, demographic impacts, public transportation, and contact patterns. Models use various mathematical tools such as ODEs, PDEs, difference equations, integro-differential equations, Markov chains, and simulations. The article also discusses the impact of different factors on TB dynamics, including the progression of TB, multiple strains, variable latent periods, and exogenous reinfection. It highlights the importance of understanding these dynamics for effective TB control and prevention. The paper also presents theoretical results and bifurcation analysis, showing how TB dynamics can be influenced by various parameters and conditions. The study emphasizes the need for comprehensive models to address the challenges of TB control in different populations and settings.