Efficient bioreactor operation is essential for biomanufacturing success. Traditional Computational Fluid Dynamics (CFD) simulations are detailed but slow and complex, limiting their use in real-time applications. This study introduces a novel unsupervised learning algorithm that clusters bioreactors into coherent regions using CFD-generated data, with the potential to incorporate real-world data. This clustering aids in identifying reactor regimes or forming the basis for compartment models. Our custom k-means algorithm ensures spatial continuity within clusters and optimizes the number of compartments based on clustering scores, maintaining clear definitions while preserving essential data. The approach’s effectiveness is demonstrated through Pareto front analysis. Validation comes from case studies on a 202 m³ Rushton impeller bioreactor and an 840 m³ airlift reactor, emphasizing the benefits of 3-D compartmentalization in capturing fluid dynamics and cellular activities. This method improves bioreactor design and scaling, boosting industrial efficiency.