A well-designed layout is a prerequisite to any successful modular building. Beyond just room arrangement, a modular building layout (MBL) requires bundling multiple rooms with prefabricated walls into transportable modules, a process that is highly risky and costly without careful consideration from the outset. Traditional methods of developing MBLs rely heavily on rules-of-thumb inherited from cast-in-situ construction, but such practices overlooked emerging data-driven smart methods. This study presents HyperMB, a hypergraph representation and learning framework tailored for supporting MBL design decisions. It first collects layout drawings from constructed modular buildings and represents them as typed hypergraphs. A reproducible floorplan-to-hypergraph pipeline is then established by treating rooms and walls as first-class nodes and encoding “modularity” as higher-order hyperedges. The hypergraph learning model then aggregates both node- and hyperedge-level features. Empirical results show that it outperforms two graph baselines on the same tasks in both within-dataset and cross-dataset settings, especially at capturing complex inter- and intra-module dependencies. HyperMB can discover latent design patterns and high-risk logistical archetypes. In generative experiments, it synthesizes modularity hyperedges with 91% validity in simple layouts, while also revealing limitations in complex module configurations. By coupling with hypergraph learning, HyperMB provides practical decision support for MBL design and planning. It lays a foundation for future automation in modular building design.