<p>Biomass-driven hydrogen production is increasingly viewed as a viable route for improving energy sustainability and reducing dependence on conventional fossil resources. In practical operating environments, however, the performance of such systems is affected by multiple interrelated factors, including temperature, pressure, feed conditions, and reaction intensity. These coupled influences make conventional performance evaluation time-consuming, especially when repeated analysis is required for dynamic operating scenarios. Meanwhile, relying only on centralized cloud-side processing may introduce additional delay, which is not always suitable for timely prediction and monitoring. To address these issues, this paper presents a machine learning assisted cloud-edge framework for performance prediction in biomass-driven hydrogen production systems. In the proposed framework, edge nodes are used for local data acquisition, preprocessing, and rapid prediction, while the cloud is responsible for model training, parameter updating, and global coordination. Such a design combines the low-latency advantage of edge computing with the stronger computing and management capability of the cloud. Experiments are conducted to evaluate both prediction accuracy and system efficiency. The results show that the proposed framework can provide reliable prediction performance for major operational indicators while reducing response latency compared with cloud-only schemes.</p>