Elastomeric bushings are common components in vehicles, used to reduce noise, vibration, and harshness. Rubber bushings are employed in suspension components such as control arm bushings, subframe bushings, and motor mount bushings, each with varying static and dynamic stiffness requirements depending on vehicle weight and ride and handling performance. Traditional rubber bush simulations typically use simple material models like hyperelastic or viscoelastic models. However, recent advancements have introduced more sophisticated material models to capture the nonlinear and time-dependent behavior of rubber materials. These advanced models may incorporate nonlinear viscoelasticity, strain rate dependency, and damage mechanics. Rubber bushings experience multiple physical phenomena simultaneously, such as mechanical loading, thermal effects, and fluid-structure interaction. New simulation techniques enable the coupling of different physics domains, allowing for a comprehensive analysis of bushing performance under realistic conditions. This paper explores the application of machine learning techniques to rubber bush simulations, aiming to enhance accuracy and efficiency. By training neural networks or other machine learning algorithms using experimental data, predictive stiffness models for rubber material behavior are developed. Overall, this new approach to rubber bush simulation facilitates more accurate and predictive analyses of bushing behavior, thereby improving the design and performance of automotive suspension systems and other applications.