Transfer Learning (TL) is a very interesting paradigm within ML that takes advantage of the knowledge learnt from solving one task to accelerate learning and improve performance on a related but distinct task. This approach uses some existing ML model trained on a vast dataset in the source domain and adapts or extends it to extrapolate its predictive power to a different domain with a smaller target dataset. This significantly reduces training time and data requirements compared to building a model from scratch. TL practices are usually linked to NNs that allow data-driven strategies to benefit from existing knowledge in related domains, improving their overall performance. TL has proven excellent results in different fields, such as medicine, mechanics, arts, physics, security or biology. Specially in tasks which involve computer vision and natural language processing techniques. TL has also been used in LIBs electrode and PEMFCs research, particularly for battery state estimations and ageing prognostics, extrapolating predictions to different domains and usage conditions. As far as we know, TL has not been used in LIBs cell or PEMFC GDLs manufacturing at the time of this writing.