Spatial-omics technologies profile cells in their native spatial context within tissues, enabling more complete understanding of cellular properties. However, a key computational challenge remains: identifying cellular interactions that underlie cell types and states, which are essential for spatial organization and provide a biologically grounded framework for understanding cell identities and spatial patterns. These interactions over different distances require multiscale modeling, which represents a major gap in existing methods. Here, we introduce Steamboat, an interpretable machine learning framework leveraging a self-supervised, multi-head attention model that uniquely decomposes gene expression of a cell into multiple key factors: intrinsic cell programs, neighboring cell communication, and long-range interactions. By applying Steamboat to diverse tissues in health and disease across various spatial-omics technologies, we demonstrate its ability to uncover critical multiscale cellular interactions, capturing classical contact signaling and revealing previously unrecognized patterns of cellular communication. Steamboat provides a powerful approach for spatial-omics analysis, offering new insights into the multiscale spatial organization of cells and their communication across a broad range of biological contexts.