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Inpatient transport is a critical but often under-recognized component of hospital operations. Delays in moving patients between wards, diagnostic areas, procedural suites, and discharge locations can disrupt clinical schedules, prolong waiting, and expose vulnerable patients to avoidable risk. Many hospital transport workflows still rely on first-in-first-out queues, dispatcher judgment, or simple proximity-based assignment. These approaches may overlook patient acuity, destination urgency, transporter workload, elevator congestion, and anticipated surges in unit-level demand. This article proposes an AI systems framework that continuously assigns a dynamic priority score to each inpatient transport request. The framework integrates patient acuity, destination urgency, transporter availability, elevator congestion, and forecasted unit-level transfer demand into a real-time dispatch logic. The framework includes a patient acuity stratification module, destination urgency classifier, transporter tracking layer, elevator congestion model, unit-level demand forecaster, and real-time prioritization engine. These components would operate as an integrated decision-support system rather than as isolated scheduling tools. The framework could support safer and more responsive transport decisions by aligning dispatch priority with both clinical risk and operational constraints. It would be expected to improve coordination among transporters, nursing units, procedural areas, and hospital command centers. A real-time AI transport prioritization framework offers a pathway toward more intelligent inpatient logistics. Its value should be evaluated through phased implementation, transparent governance, and careful assessment of clinical and operational consequences.