Train derailments pose a critical failure mode in railway systems, often resulting in severe safety hazards and significant financial losses. Understanding how train loading patterns interact with track deficiencies is essential for effective failure analysis and prevention. This paper introduces the Rapid Vehicle–Track Interaction (R-VTI) as a framework to simulate the complexities of dynamic train–track interactions. Central to the framework is the novel Pseudo-Dynamic Coupling (PDC) technique, which enables computation of wheel–rail dynamic forces with substantially greater computational efficiency than currently used coupling techniques. The R-VTI framework supports a wide range of solver techniques and subsystem coupling schemes, making it adaptable for different simulation requirements. The framework is validated against Federal Railroad Administration field measurements, achieving agreement within 5% error. A case study of different train–track configurations shows that the framework can quickly detect when loading patterns and track conditions exceed derailment thresholds. Axle-level results reveal that unloaded cars near the front or middle of the train increase the likelihood of derailment-failure modes. The efficiency of the R-VTI framework enables large-scale scenario analysis, supporting both optimized loading strategies and targeted track maintenance. By providing a robust and scalable solution, the R-VTI framework advances derailment potential assessment practices, offering a practical tool for improving railway safety and operational resilience.