The present work optimizes the global chemical mechanism of methane MILD combustion from Jones and Lindstedt (Combust. Flame 1988, 73, 233-249), named "JL", using artificial neural network (ANN) for computational fluid dynamics (CFD) simulations. Such an optimized JL mechanism, abbreviated as "JL-ANN", is obtained by ANN searching for the optimal reaction parameters that lead to the results matching those from GRI-Mech 3.0, the detailed mechanism for burning methane in a plug flow reactor. This JL-ANN mechanism is then checked by comparing its performance with that of GRI-Mech 3.0 and that of previous JL mechanisms whose reaction parameters were refined in various CFD simulations against experimental measurements available for reference. Results demonstrate that JL-ANN performs significantly better than all the previous JL mechanisms for numerical simulations of both a nonpremixed methane-jet flame in hot coflow and in-furnace MILD combustion. Therefore, the ANN method can be considered as a promising tool in optimizing various global mechanisms of combustion chemistry for CFD simulations of MILD combustion or any mode of combustion.