This study presents a simulation method for reproducing slush accumulation on underbody components, with a particular focus on the floor undercover, during vehicle operation on slush-covered roads. As electrified vehicles become increasingly important in the pursuit of carbon neutrality, the adoption of aerodynamic undercovers to improve driving range has accelerated. However, these components are exposed to various environmental stresses, including water, chipping, and especially snow and slush, which can lead to damage and performance degradation. While previous research has addressed water and chipping stresses through simulation, studies on slush-induced stress have been limited. To address this gap, the Moving Particle Semi-implicit (MPS) method was applied, incorporating a power-law model to represent the non-Newtonian flow characteristics of slush. Parameter identification was conducted through steel ball drop tests and tire scattering tests, ensuring both qualitative and quantitative agreement between experimental and simulation results. The simulation’s accuracy was further validated by comparing the scattering direction and accumulation locations with those observed in actual vehicle tests. The method was also applied to different floor undercover specifications and multiple vehicle models, demonstrating its versatility and independence from vehicle type. Quantitative evaluation of slush accumulation was achieved, and the simulation results showed excellent agreement with experimental data across all tested conditions. This Computer-Aided Engineering (CAE) approach enables efficient and highly accurate assessment of underbody component stress during slush road driving, supporting both aerodynamic performance and environmental durability in the development of electrified vehicles. Remaining challenges include the variability of slush properties under real-world conditions, the limitations of the power-law model, and computational costs associated with the MPS method. Further research is required to enhance the method’s accuracy and applicability.