In a wide range of real-world physical and dynamical systems, precise defining of the uncertain parameters in their mathematical models is a crucial issue. It is well know that the usage of fuzzy differential equations (FDEs) is a natural way to exhibit these possibilistic uncertainties. In this research, a fast and accurate type of Runge-Kutta methods is generalized that are
for solving first order fuzzy dynamical systems An interesting feature of the structure of this technique is that the data from previous steps are exploited that reduces substantially the computational costs. The major novelty of this research is that we provide the conditions of the stability and convergence of the method in the fuzzy area which significantly completes the previous findings in the literature. The experimental results demonstrate the robustness of our technique by solving linear and nonlinear uncertain dynamical systems.