Gas–liquid–solid (G-L-S) mechanically agitated reactors are commonly used in chemical, pharmaceutical and bioprocessing applications due to their low operating costs and controlled and effective mixing. Computational Fluid Dynamics (CFD) is a powerful tool that enhances the understanding of flow dynamics, phase interactions and reactor performance. However, the CFD modeling of G-L-S mechanically agitated reactors is not extensively studied in the literature due to complex multiphase interactions, along with reactor design variations. This paper provides a critical synthesis of the literature, offering an overview not only of G-L-S stirred tank CFD modeling approaches but also of practical guidance on their selection and validation. Emerging high-resolution experimental techniques such as Electrical Resistance Tomography (ERT) coupled with pressure transducers, and Machine Learning (ML) models combined with experimental data, look promising to overcome current three-phase validation limitations. Future work to enhance predictive capabilities and reactor design and operation includes developing real-time digital twins, physics-based ML models and/or hybrid CFD-ML models.