The promise of biomass-derived biofuels is often overshadowed by intricate material handling challenges such as hopper clogging and screw feeder jamming. These handling issues stem from the knowledge gap among particle-scale material properties (e.g., particle size), bulk-scale material attributes (e.g., relative density), macro-scale equipment design (e.g., hopper inclination), and flow performance (e.g., probability of clogging). This work combines physical experiments, validated numerical simulations, and data augmentation to develop a machine learning-based hopper design for flowing granular woody biomass materials. The flow behavior of granular biomass is simulated and validated against physical tests utilizing the developed smoothed particle hydrodynamics (SPH) solver and a modified hypoplastic model. A comprehensive evaluation of the flow performance, including flow rate, flow stability, and flow pattern, is conducted on an extensive data set encompassing various biomass particle sizes, moisture contents, relative densities, and hopper operating conditions. A feed-forward neural network is trained and optimized with this data set to correlate cross-scale attributes with the flow performance metrics. The results reveal promising predictive accuracy on seen and unseen data sets. Further evaluation of how various input attributes affect the predicted flow performance metrics is carried out. The results indicate that hopper opening width primarily dictates flow throughput, while relative density, wall friction, inclination angle, and hopper opening width collectively impact flow stability. Additionally, flow patterns are predominantly governed by relative density, wall friction, and inclination angle. Moreover, the clogging potential is found to be exclusively characterized by the index dedicated to flow stability. The combination of high moisture contents, dense packing, smooth wall friction, low inclination angles, and small hopper opening widths substantially elevates the risk of unstable flows and clogging. This study serves as a potent design tool for flowing milled woody biomass materials in hoppers for all stakeholders in biorefineries and equipment manufacturing.We present a machine learning-based design tool for granular biomass flow in wedge-shaped hoppers to enhance material handling for biofuel production.