Many diseases, such as obesity, have systemic effects that perturb multiple organ systems throughout the body. However, tools for comprehensive, high-resolution analysis of disease-associated changes at the whole-body scale have been lacking. Here, we developed MouseMapper, a suite of foundation model-based deep learning algorithms, to enable a multi-system analysis of disease across the entire mouse body. MouseMapper enables quantitative, graph-based analysis of nerves and immune cells at the whole-body level resolving fine axonal branches and immune cell clusters and automatically segments 31 organs and tissues in the mouse body.  We applied MouseMapper to study high-fat diet-induced obesity, uncovering a significant degeneration of the infraorbital branch of the trigeminal ganglia. This structural damage in infraorbital nerves was associated with functional sensory deficits in whisker sensing. Furthermore, we identified proteomic changes in the trigeminal ganglion affecting axon remodeling and complement pathways both in mouse and human. MouseMapper also generated detailed 3D inflammation maps by characterizing immune cell cluster compositions across tissues. The MouseMapper framework demonstrates robust generalizability across different imaging resolutions and datasets. Our study provides a powerful, scalable approach for discovering and quantifying systemic pathologies, bridging molecular insights from animal models to human conditions.