<p>Rubber-metal bushings (RMB) are critical components in multi-body systems, such as vehicles and industrial machinery, due to their ability to enable relative motion, dampen vibrations, and transmit forces. However, their nonlinear behavior challenges accurate modeling. Traditional physics-based models often fail to balance simplicity, accuracy, and computational efficiency. The growing availability of experimental data offers opportunities to improve RMB modeling through hybrid and data-driven approaches. This study evaluates physics-based, hybrid, and data-driven methods based on predictive accuracy, modeling effort, and computational cost. Hybrid approaches, combining machine learning techniques with physics-based models, are investigated to leverage their complementary strengths. Results show that hybrid methods enhance accuracy for simpler models with a modest increase in computational time. This highlights their potential to simplify RMB modeling while balancing accuracy and efficiency, offering insights for advancing multi-body system simulations. Building on these insights, data-driven methods are explored for their ability to provide surrogate models for dynamical systems without requiring expert knowledge. Experiments reveal that while simple data-driven methods approximate system behavior when data has low variance, they fail with trajectories of widely varying frequency and amplitude.</p>