The rapid evolution of electric vehicles (EVs) has necessitated innovative approaches to optimize ride comfort, handling, and overall suspension performance. Unlike conventional internal combustion engine vehicles, EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics. This paper presents an advanced damping force modeling methodology leveraging machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The study employs data-driven ML algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, to model the nonlinear and frequency-dependent behavior of dampers under various operational conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. The proposed ML-based approach predicts damping forces with high accuracy, outperforming traditional analytical models in both speed and precision. Additionally, this research evaluates the impact of machine-learned damping force predictions on critical ride and handling metrics, including ride comfort, road-holding ability, and energy efficiency. The results demonstrate that ML models can significantly improve design iterations, enabling the development of adaptive suspension systems tailored to the specific demands of EVs. This paper contributes to advancing the state-of-the-art in suspension modeling by integrating ML-driven insights into the EV development cycle. It highlights the potential of artificial intelligence to transform suspension design, paving the way for superior ride quality and vehicle performance in electric mobility.