Earthquakes significantly impact societies and economies, underscoring the need for effective search and rescue strategies. As AI and robotics increasingly support these efforts, the demand for high-fidelity, real-time simulation environments for training has become pressing. Earthquake simulation can be considered as a complex system. Traditional simulation methods, which primarily focus on computing intricate factors for single buildings or simplified architectural agglomerations, often fall short in providing realistic visuals and real-time structural damage assessments for urban environments. To address this deficiency, we introduce a real-time, high visual fidelity earthquake simulation platform based on the Chaos Physics System in Unreal Engine,specifically designed to simulate the damage to urban buildings. Initially, we use a genetic algorithm to calibrate material simulation parameters from Ansys into the Unreal Engine’s fracture system, based on real-world test standards. This alignment ensures the similarity of results between the two systems while achieving real-time capabilities. Additionally, by integrating real earthquake waveform data, we improve the simulation’s authenticity, ensuring it accurately reflects historical events. All functionalities are integrated into a visual user interface, enabling zero-code operation, which facilitates testing and further development by cross-disciplinary users. We verify the platform’s effectiveness through three AI-based tasks: similarity detection, path planning, and image segmentation. This paper builds upon the preliminary earthquake simulation study we presented at IMET 2023, with significant enhancements, including improvements to the material calibration workflow and the method for binding building foundations.