ABSTRACT Viscosity is a fundamental physical property that governs the flow and processing behavior of glass‐forming liquids. During glass manufacturing processes, it is thus important to know how rheological properties are influenced by variables such as temperature, chemical composition, and the presence of any inclusions within the melt. There is extensive literature describing theoretical models for calculating the viscosity of oxide glass‐forming systems; resources dedicated to experimental methodologies for measuring oxide glass viscosity are comparatively scarce. This review therefore focuses on direct viscometry techniques as well as indirect approaches for measuring viscosity including differential scanning calorimetry (DSC) and dilatometry combined with the Mauro–Yue–Ellison–Gupta–Allan (MYEGA) model. In addition, we highlight emerging machine‐learning approaches, which offer complementary insights into glass viscosity, particularly in complex multicomponent systems. Such machine learning‐based methods require large and high‐quality datasets for training and validation, underscoring the vital importance of experimental measurements in establishing reliable viscosity values. Overall, the primary goal of this review is to fill a gap in the literature regarding experimental methodologies for viscosity measurement of oxide glasses, explaining their underlying principles, highlighting the challenges faced by researchers, and emphasizing the continued necessity of experimental viscosity measurements alongside theoretical and computational approaches.