Liquid holdup (HL) prediction in gas–liquid two-phase flows (TPF) has been studied extensively for decades. However, existing reviews and empirical correlations have largely treated key controlling parameters, particularly liquid viscosity and pipe in-clination, as independent or secondary factors. This review is based on prior studies by providing a systematic synthesis of the coupled effect of high viscosity (200–800 mPa·s) and pipe inclination (0° to 90°) on both general liquid holdup (HL) and slug liquid holdup (HLs). These effects are regime-dependent: negligible in low-viscosity flows but dominant in high-viscosity, large-diameter, and undulating pipelines. The review identifies two critical limitations of current models: their systematic underprediction for high-viscosity fluids and their failure to account for inclination-driven HL varia-tions, which can be as high as 10–30%. Consequently, this review advocates a paradigm shift toward data-driven intelligent models (e.g., Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs)) trained on comprehensive, well-curated datasets that explicitly capture the viscosity–inclination coupling. These hybrid models, which combine data-driven learning with physical constraints, provide the most viable path to overcome the fundamental limitations of current correlations and achieve ac-curate HL and HLs prediction for the design and operation of real-world, undulating pipeline systems handling viscous fluids.
