The strategies for model order reduction in controller design are the main emphasis of this work. Model order reduction is a method for decomposing complex, high-dimensional control system models into simpler, more computationally efficient parts. We go over a number of methods, including factor division, balanced truncation, Krylov subspace methods, proper orthogonal decomposition, continued fraction expansion, Pade approximation, Routh stability criterion, differentiation method, Mihailov stability criterion, and Routh-Hurwitz array method. In order to demonstrate how these techniques may be used to simplify a system, we give a numerical example of a fourth-order system. Using the reduced-order models, we develop and simulate a controller for the system. Additionally, we go through the value of model order reduction in streamlining the design and analysis of control systems and enhancing their computational effectiveness. Finally, we offer a list of sources for additional reading on the subject.