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Hospital operations face escalating demands for efficient resource allocation amid fluctuating patient volumes, staff shortages, and constrained budgets. This conceptual manuscript introduces the predictive resource allocation governance scaffold (PRAGS), a theoretical architecture designed to integrate artificial intelligence (AI) driven predictive analytics into hospital governance frameworks. PRAGS emphasizes proactive resource orchestration through layered intelligence modules, interoperability protocols, and continuous monitoring loops to mitigate operational inefficiencies. Drawing on clinical AI architectures and healthcare analytics infrastructures, the scaffold outlines a multi-tiered system comprising predictive engines, governance oversight layers, and adaptive feedback topologies. Key components include decision-support pipelines that forecast resource needs, EHR-intelligence ecosystems for data harmonization, and interoperability frameworks that ensure seamless integration across hospital departments. The architecture addresses governance challenges such as ethical AI deployment, bias mitigation, and regulatory compliance without empirical validation. By using interpretive formulas to model resource allocation dynamics, decision latency, and governance load, PRAGS provides a blueprint for enhancing hospital resilience. This work synthesizes recent literature on AI governance and clinical workflows and proposes a scaffold that fosters equitable resource distribution while prioritizing patient safety and operational sustainability. Ultimately, PRAGS offers a conceptual pathway for hospitals to transition toward intelligent, governed resource management systems.