The purpose of this paper is to provide a comprehensive review of numerical methods for solving fuzzy and dual-fuzzy nonlinear equations. These approaches are crucial in modelling uncertainty in real-world applications, such as engineering, optimization, and decision-making. For fuzzy nonlinear equations, the primary emphasis focuses on iterative numerical approaches. These techniques include Newton's method, the steepest descent method, the homotopy method, Broyden's method, the fixed Newton method, and the improved false position method. We have also developed approaches for solving dual fuzzy nonlinear equations. These approaches encompass the Quasi-Newton method, the Broyden-Newton method, and the Modified Newton method. Numerical experiments evaluate the effectiveness, convergence behaviour, and computing complexity of different approaches. In the majority of test situations, our findings demonstrate that both the Newton method and the Fixed Newton technique achieve convergence in fewer than five iterations. On the other hand, the homotopy approach requires well over one hundred iterations in specific scenarios, illustrating the giant disparities in efficiency between the two methods. Additionally, the analysis identifies the conditions under which each method achieves optimal performance. This paper provides a comparative analysis, highlighting alternatives in terms of accuracy and computing cost. This review contrasts with prior studies, which have focused on specific methods. These findings guide in selecting suitable numerical techniques for solving nonlinear equations in fuzzy or dual-fuzzy environments.