The ML-based surrogate modeling framework presented in this article has a similar objective like the ones we previously published for the electrode slurry and the electrode calendering: simulate the electrode processing in 3D and as a function of time at the mesoscopic level, with significantly reduced computational costs in comparison to full traditional numerical simulations such as CGMD or DEM. These frameworks pave the way towards quasi-real time simulation of the electrode microstructure formation process. Additionally, another application can emerge thanks to our framework: by leveraging electrode inline characterization of the mass loading profilometry (giving information about the areal mass loading heterogeneities coming out from the oven) and our mesoscopic surrogate models, it could make possible to have a real time estimation of the local internal architecture of the electrode coming out from the oven, characterized by its tortuosity factor or conductivity. This approach would require the training of our surrogate model on more drying conditions to give to this concept more flexibility. The coupling of the inline characterization tool with our modeling framework can allow the characterization tool to provide also information about the internal electrode properties in real time. Indeed, we believe that the physics-ML coupling and computational cost of our mesoscopic modeling framework, paves the way towards the concept of physics-assisted digital twin for battery manufacturing, where the manufactured electrodes are characterized in real time to inform models giving feedback to the machinery to improve the quality of the produced electrodes.