Understanding Li-ion transport through the solid electrolyte interphase (SEI) is essential for improving the stability of lithium-metal batteries, as nonuniform ionic transport through the inorganic SEI can promote spatially localized Li deposition and dendrite formation. In this work, we develop a deep-learning-assisted framework to model Li-ion transport across the inorganic SEI by combining density functional theory (nudged elastic band) calculations with graph neural network learning. A systematic diffusion dataset was first generated for eight major inorganic SEI compo-nents, namely, LiF, LiCl, LiBr, LiI, Li2O, Li2S, Li3N, and Li2CO3, covering both bulk (grain) diffusion and grain-boundary diffusion over representative low-energy surfaces and interfaces. This dataset includes homogeneous and heterogeneous interfaces (in-terfaces), providing a unified design space for Li-ion migration in SEI environments. A path-aware graph variational autoencoder (GVAE) was then used to learn latent representations of NEB trajectories, and the learned embeddings were incorporated one into a predictive GNN model for minimum-energy-path and migration-barrier estimation. The combined GVAE-GNN framework achieved strong predictive performance for both grain and grain-boundary diffusion, with test-set R2 values of 0.93 and 0.94, respectively. Feature-importance and latent-space analyses further show that migration behavior is governed not only by composition, but also by local structural factors such as exposed surface, saddle-point character, and grain-boundary energetics. The results reveal clear transport trends across SEI chemistries, with halide-rich systems generally associated with narrower low-barrier distributions, while Li3N, Li2CO3, and structurally mismatched heterogeneous interfaces exhibit broader and higher-barrier landscapes. This study give a physics-informed machine-learning framework for Mapping and predicting Li-ion migration in complex SEI structures and provides insight into how SEI chemistry and microstructure jointly control interfacial ion transport.