Hydrogen-based shaft furnace technology holds promise for low-carbon hydrogen metallurgy. Since hydrogen-assisted iron ore reduction is highly endothermic, inadequate heat supply relevant to the contact of gas and densely packed ores may reduce the rate and efficiency of reductions. The key to addressing this issue lies in understanding the competition among heat supply, heat transfer, and heat loss driven by the gas flow around ores and reactions within them. Modeling and simulation are crucial for revealing the underlying mechanisms and promoting process scale-up and intensification. This review summarizes previous efforts in physical modeling and model applications for improving the reduction performance. The discrete element method (DEM) and computational fluid dynamics (CFD)− DEM models have been used for particle-scale simulation to investigate inhomogeneous particle descent and relevant particle−particle interactions. For macroscale simulations, steady-state simplified models such as plug flow and REDUCTOR, as well as the Eulerian two-phase model, have been developed by considering heat and mass transfer. Based on these model applications, strategies including the optimization of operating conditions and gasfeeding methods have been proposed to improve the furnace performance. Further numerical efforts are needed to analyze the infurnace heat evolution and reduction and reveal the competitiveness of flow, transport, and reaction by incorporating multiscale physics in shaft furnaces. Additionally, attention could be paid to the effects of particle sticking and degradation on reduction, which may be more serious when the proportion of lump ores increases. When evaluating relative optimization strategies, comprehensive comparisons are expected in terms of iron ore reduction degree, gas utilization rate, energy consumption, and economic feasibility under various reducing and cooling gas operating conditions and furnace profiles to offer practical guidelines for industrial design and intensification.