In the rapidly evolving and highly competitive automotive industry, manufacturers are under immense pressure to bring products to market quickly while meeting customer expectations. As a result, optimizing the product development timeline has become essential. Structural integrity analysis for chassis and suspension systems lies in the accurate acquisition of operational load spectra, conventionally executed through Road Load Data Acquisition (RLDA) on instrumented vehicles subjected to proving ground excitation. At this point, RLDA is mainly used for final validation and fine-tuning. If any performance shortfalls, such as premature component failure or durability issues, are discovered, they often trigger design revisions, prototype rework, and additional testing This study proposes a Virtual Road Load Data Acquisition (vRLDA) methodology employing a high-fidelity full-vehicle multibody dynamic (MBD) representation developed in Adams Car. The system is parameterized and uses high-resolution F-Tire models to replicate transient tire-road interactions, digital tracks are derived from LIDAR-based topography of durability test tracks. Boundary conditions replicate vehicle drive speed and payload. Attachment point load are extraction and its statistical signal fidelity assessed via RMS error metrics, relative damage and peak amplitude congruence against physical RLDA data. Results demonstrate high correlation across critical load channels, accelerations, LVDT & validating the computational workflow's capacity to replicate operational durability environments. The vRLDA approach thus provides a flexible, scalable architecture to support pre-validation of suspension modules, enabling the design verification, reduction in prototype, instrumentation dependency, and improved convergence of CAE-based life prediction models with empirical outcomes.