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Accurately modeling the viscosity of shear-thinning elastomer compounds is crucial for optimizing their performance in industrial applications. Classical models (Power Law, Carreau, Cross) cannot directly account for temperature, and when coupled with temperature models (Arrhenius, WLF, VTF), they become mathematically complex and computationally costly for simulations. This study introduces the Multi-Variable Implicit (MVI) viscosity model, derived using machine learning-based symbolic regression. It offers a simplified mathematical structure while maintaining high predictive accuracy. The MVI model implicitly incorporates both shear-rate and temperature dependence in a single expression, while also capturing zero-shear viscosity through a Newtonian plateau. This feature ensures numerical stability and yields physically meaningful results across a wide range of shear rates and temperatures. The model was trained on high pressure capillary-rheometer data of an elastomer (70–120 °C; 10-5000 1/s), corrected for entrance losses and non-Newtonian profiles, and extended with synthetic points from a Carreau-Arrhenius fit to include the low-shear plateau. On test data, MVI achieved R² = 0.99 while preserving finite zero-shear viscosity. Independent validation on different materials across wider ranges gave R² = 0.957–0.990 without retraining, confirming strong generalization. To demonstrate its practical applicability, capillary-flow CFD simulations were carried out using both the MVI and Carreau-Arrhenius models. While each gave accurate predictions of the pressure-flow response, the MVI model required less computational effort because it avoids repeated exponential calculations. These findings highlight the MVI model as a novel, efficient, and practical solution for viscosity modeling in polymer processing and other fluid flow applications.