In the case of LIB cells manufacturing, the ARTISTIC project has been a pioneer in optimizing these processes with different computational modeling techniques at the mesoscale. The ARTISTIC project simulates the entire manufacturing chain of electrode and cell, from the slurry preparation, passing by its drying, its calendering, its electrolyte filling, to reach the resulting cell performance. These physics-based computational models simulate and predict the influence of materials properties and manufacturing parameters on the electrode and cell properties. They have been calibrated and validated against experimental data. To face the computational cost that these models can have, data-driven Machine Learning (ML) surrogate models have also been trained with the physics-based models obtained datasets to accelerate the optimization of different manufacturing aspects. Recently, in the LIB cell field, a Deep Learning (DL) model has been trained to track microstructure evolution over time during the electrode calendering by using Discrete Element Method (DEM) time series data. Also, for PEMFCs, Random Forest (RF) models have been trained to predict different properties of the GDLs as a function of their most relevant manufacturing parameters. Since the numerous variables impacting the final electrode quality and performance, optimizing these processes with, for example, a trial-and-error method is not straightforward. In this scenario, ML techniques are excellent tools to unveil patterns and relationships hidden in this data and to optimize these processes. ML algorithms have a huge potential in other critical domains, including applications such as material discovery, real-time monitoring, state estimation, battery usage, fault detection or life cycle management. As we have previously demonstrated in our ARTISTIC project initiative, ML can be used to accelerate both the understanding of new materials (chemistry and formulation), and the optimization of LIB electrode and cell manufacturing process. Within ML, supervised ML techniques require labeled data, where each input data point has an associated output value, to enable data-driven predictions and optimizations. On the other hand, unsupervised ML uses unlabeled data to find patterns in it. For LIB cells, both mentioned types of ML algorithms to uncover the interdependencies between slurry parameters and NMC final properties. Duquesnoy et al., in addition to considering slurry parameters, included coating ones to develop an automatic methodology that used a Gaussian Naives Bayes classifier to determine if the resulting electrode is homogeneous or heterogeneous. Furthermore, K-Means clustering was used in the calendering step of NMC-based electrodes by Primo et al.. For graphite anodes, Faraji-Niri et al. collected a lab-scale dataset containing control variables in the slurry and coating stages to predict final properties of the electrode through the use of RF models. For the State of Health (SoH) estimation of commercial LGM50 cells, a Gaussian Process Regressor was trained by Faraji-Niri et al. by selecting features of electrochemical impedance spectroscopy (EIS) data. Moreover, Neural Networks (NNs) have been developed to detect cracks in LIB electrodes by using 3D image data and to map the 3D architecture of NMC particles with focused ion beam slicing in sequence with electron backscatter diffraction data. Meanwhile for GDLs, various literature works show the use of ML models for different applications. For instance, Hou et al.