This study explores the innovative integration of artificial intelligence (AI) into the preliminary design of automotive suspension systems, focusing on vibration reduction and performance optimization. Unlike traditional methods, which often rely on static models and costly iterative testing, this research proposes a dynamic and data-driven approach, leveraging AI capabilities to enhance the precision and efficiency of the design process. A comprehensive parametric study is conducted to identify and analyze the most influential suspension parameters, such as stiffness, damping, geometry, and mass distribution. The Anaconda platform is used for data processing, modeling, and simulation, enabling a systematic analysis of the interactions between these parameters and their impact on the system’s dynamic behavior. AI algorithms facilitate predictive analysis of vibrational behaviors and real-time adjustments, optimizing the system’s response to varying road conditions. This approach not only enhances ride comfort but also contributes to the durability of components. Furthermore, by narrowing the intervals of each parameter through AI integration, the study ensures more precise optimization compared to traditional parametric studies. Detailed case studies demonstrate how AI outperforms conventional techniques in terms of precision, adaptability, and efficiency. Ultimately, the combination of parametric analysis and AI technologies offers new insights into the design of smarter, more efficient suspension systems, paving the way for more sustainable and innovative solutions in automotive engineering.