All-solid-state batteries (ASSBs) are high-energy, high-power batteries. To enhance the understanding of the electrochemical-mechanical behavior in ASSBs across different scales, we developed a multi-physics and multi-scale modeling framework. This framework incorporates elastoplastic finite deformation and electrode microstructures of ASSBs, and the role of gradient plasticity in the governing equation for multiple physical fields was discussed. Utilizing X-ray computed tomography, we reconstructed the microstructure through a machine learning (ML)-informed image segmentation process. Our study clarifies the impact of electrode microstructures on concentration, stress, voltage, delamination and buckling from AM to electrode scale. Comparative analysis of the Feret diameter distribution of active materials (AMs) shows that ML-informed image segmentation outperforms two traditional segmentation methods. We observed that the asynchronous diffusion saturation of AMs, varying in shape and size, significantly influences the electrochemical-mechanical behavior of ASSBs, resulting in complicated debonding indices and J-integral distribution at the interface. The proposed upscaling homogenization procedure is demonstrated to be efficient for buckling analysis, with the shape mode closely matching existing experimental observations. These results shed light on the critical multi-physics and multi-scale coupling mechanisms in ASSBs.