Computational Fluid Dynamics (CFD) is extensively utilized in large-scale bioreactor fermentation to optimize hydrodynamics, mass transfer, and mixing efficiency while minimizing shear-based damage to cells and other constituents. CFD employs multiphase models, including Eulerian–Eulerian framework and turbulence models like k-ω shear stress transfer (SST), to simulate complex fluid dynamics, including impeller rotation and gas sparging. It can estimate parameters such as volumetric oxygen transfer coefficient (kLa), power consumption, and mixing durations, enabling the manufacturer to adjust operational parameters (such as impeller velocity and aeration rates) and bioreactor configurations. CFD simulations have been demonstrated to deliver significant savings in energy (up to 50%), improve mass transfer uniformity, facilitate process scale-up by predicting gas-liquid interactions, mitigate dissolved oxygen gradients, and reduce risks of impeller flooding or foam formation. In this chapter, we review recent advancements on this topic. Considering the recent popularity of machine learning applications, we also discuss how these will influence CFD methodologies.