<p>Maintaining an adequate blood supply is critical for health care systems, but clinical blood demand is influenced by dynamic, multilevel factors. The aim of this study was to identify and quantify the relationships among the factors that affect clinical blood demand in Suzhou, China, to increase predictive accuracy and supply chain resilience. From March to April 2023, 422 experts in transfusion medicine were surveyed using the Delphi method, and the data were analyzed via exploratory and confirmatory factor analyses (EFA/CFA) to construct a hierarchical structural model. The results revealed a three-layer model in which factors were categorized by temporal impact. Short-term factors (blood transfusions, sudden disasters) exhibited the greatest direct influence (path coefficients of 0.682–0.899) driven by surgical volumes and acute disaster responses. Medium-term factors (medical resources, beds, population) had significant impacts (path coefficients of 0.780–0.834) and linked demographic shifts and health care capacity to blood utilization. Long-term factors (environmental) had indirect effects (path coefficient of 0.623) and shaped demand via societal and infrastructural changes. The two-factor analysis model demonstrated nonlinear interactions and hierarchical transmission mechanisms and emphasized the synergistic increase in medical resource allocation and surgical complexity on demand. These findings highlight the need for differentiated monitoring strategies, including real-time tracking of short-term fluctuations, periodic assessment of medium-term drivers, and policy adjustments for long-term trends. While the model offers a robust framework for adaptive blood management in Suzhou, generalization to smaller cities or regions with different health care infrastructures requires further validation. This study advances precision forecasting and collaborative resource allocation and supports resilient blood supply chains in the context of megacities.</p>