In this research, we extend a previously validated methodology for identifying vehicle suspension parameters from simpler quarter-vehicle and half-vehicle models to a comprehensive full-vehicle model. This study focuses on the application of synthetic data to perform non-invasive parameter identification, addressing the increased complexity inherent in the full-vehicle model due to its more intricate vertical dynamic equations and a larger set of parameters. Our approach employs a refined local optimization algorithm to ensure precise simulation of vehicle dynamics and accurate parameter identification. The full-vehicle model’s complexity presents new challenges, including heightened nonlinearity and the impact of different objective functions on the identification process. Our findings highlight the robustness and adaptability of the synthetic data-based methodology while also delineating the limitations encountered in this more demanding scenario. These insights contribute to the ongoing development of efficient and cost-effective solutions for dynamic vehicle parameter identification, essential for predictive maintenance and advanced vehicle design.