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Hospital congestion is an interconnected operational phenomenon in which local delays can cascade across emergency, inpatient, diagnostic, pharmacy, and consultative services. Prediction has often been organized around individual departments rather than the hospital as a coupled system. Current forecasting approaches do not sufficiently represent dynamic dependencies among units, including patient transfers, service requests, staffing availability, and real-time capacity. This limits their ability to anticipate how pressure in one department could propagate into another. This article proposes a temporal graph transformer for forecasting interdepartmental workflow congestion across a hospital network. The model is designed to learn from time-varying departmental relationships and produce forward-looking congestion estimates for operational decision support. The proposed framework represents hospital units as graph nodes annotated with capacity, queue, and staffing signals. Directed edges are weighted by patient movement and service request dependencies, and a temporal graph transformer processes graph sequences to estimate future congestion probabilities for each unit. Conceptually, the model would be expected to identify building pressure before it appears as visible delay. Such forecasts could support proactive coordination, including staff redeployment, transfer prioritization, and service sequencing. A graph-based temporal perspective could shift hospital congestion management from reactive firefighting toward coordinated, predictive flow control. The proposed model provides a conceptual foundation for future prospective evaluation in real-time operational settings.