formulated an Extreme Learning Machine (ELM) model that allowed to obtain the optimal GDL structure parameters with the minimum temperature, maximum current density, and good oxygen concentration uniformity. Shum et al. used DT and convolutional NN (CNN) algorithms to segment GDL's X-ray computed tomography (CT) image stacks, comparing their performance with basic image processing techniques. In addition, Cawte et al. developed a 3D CNN to predict the GDL materials' permeability directly from 3D binary image data. While Froning et al. also used a CNN model to predict the GDL materials' permeability, but with stochastically generated microstructures. Saco et al. tested and observed SVM regression, Linear Regression (LR) and k-Nearest Neighbors algorithms on different humidification processes during experimental studies of PEMFCs. They concluded that, in their case of study, LR provided better accuracy that other models. Furthermore, for deformed GDL, Wang et al. Built an M 5 model, which includes multi-physics and multi-phase flow simulation, ML-based surrogate modelling, multi-variable and multi-objects optimization. They also compared response surface methodology (RSM) and artificial NN (ANN) methodologies. This M 5 model proved to be effective and efficient for optimizing the GDL current density and oxygen distribution. It is worth mentioning that all these previous works have emphasized the development of ML tools to analyze vast datasets. Surprisingly, none of the previous works addressed the problem of how to derive reliable ML models with smaller datasets, arising either from the lack of enough experiments or from the computational cost of running large simulations.