Understanding how metabolites, lipids, and glycans shape physiology requires spatially resolved analysis across molecular classes. We developed a single-tissue workflow for spatial mass spectrometry imaging that captures the metabolome, lipidome, and glycome in parallel. This pipeline is powered by the Spatial Augmented Multiomics Interface (SAMI), a computational platform for multiomics integration, high-dimensional clustering, spatial annotation, and pathway enrichment. Using SAMI, we mapped metabolic diversity across brain regions and revealed distinct alterations in the Ps19 Alzheimer’s disease mouse model. To extend spatial biology beyond two dimensions, we created MetaVision3D, an AI-guided pipeline that transforms serial 2D MALDI-MS sections into high-resolution 3D spatial metabolomes. By aligning, normalizing, and interpolating tissue sections, MetaVision3D reconstructs tissue-wide metabolite distributions at submesoscale resolution. As proof of principle, we generated a 3D atlas of brain metabolism in normal and diseased mice, available as an interactive online resource. Together, these technologies provide a framework to explore spatial metabolic networks and illuminate biochemical complexity in health and disease.