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Foundation models enable knowledge transfer across data modalities and tasks, yet foundation models for spatial biology remain in their early stages, largely centered on encoding single-cell representations in spatial context without fully integrating transcriptomic and morphological information to delineate functional niches. Here we introduce SpatialFusion, a lightweight multimodal foundation model that identifies biologically coherent microenvironments defined by distinct pathway activation patterns rather than spatial proximity alone. SpatialFusion integrates paired histopathology, gene expression, and inferred pathway activity into a unified representation. Compared with two specialist niche-detection methods and four spatial foundation models, SpatialFusion performs competitively and consistently resolves fine-grained spatial niches with unique pathway-level signatures. Applying the model to two Visium HD cohorts uncovered a pre-malignant niche in morphologically normal mucosa adjacent to colorectal tumors and revealed distinct malignant microenvironments in non-small cell lung cancer that were predictive of tumor stage. Overall, SpatialFusion offers a versatile framework for multimodal spatial analysis, enabling the discovery of new morpho-molecular niches with significant biological and clinical relevance.