A ML-based optimization strategy is implemented to efficiently identify optimal parameter sets. This two-step process involves: (1) establishing a robust correlation function between input and output parameters, achieved using a combination of Random Forest Regressor and Ada Boost Regressor, aiming for an R-squared score exceeding 0.9 for all predictions. (2) Utilizing the established correlation function to guide an optimizer towards optimal parameter sets based on predefined conditions. The NM method is chosen for this purpose, alongside a grid search approach to mitigate the risk of getting trapped in local minima. We then compare the GDL properties using the results from our optimization workflow and the physics-based digital characterization of stochastically generated GDL microstructure. Both used the optimal input parameters from the five cases mentioned above as input parameters.