Ensuring rail infrastructure integrity is crucial for passenger safety and freight transport efficiency. Continuous monitoring of railway track components, therefore, plays a vital role in ensuring safe and reliable railway operations. Traditional inspection methods, such as manual and visual inspections, often miss subtle defects, especially when track components are obscured due to harsh environmental conditions, leading to increased risks and costly maintenance. To address these challenges, a differential eddy current sensor was developed that can be mounted on in-service trains for regular inspections. The current study aims to enhance the knowledge of the sensor capability by developing data driven and multi-physics simulation models to analyze the magnetic response of critical track components and their defects. Comprehensive simulations will consider interactions among the sensor, track components, operational and environmental conditions, and train dynamics, thus ensuring robust and resilient infrastructure. This paper proposes a methodology for developing a baseline multiphysics simulation model to generate synthetic data, enhancing and complementing data-driven approaches to improve the sensor system’s detection capabilities, addressing the need for an efficient inspection procedures and optimized maintenance practices. The findings highlight the potential of Multi-physics simulations in detecting critical rail defects and advancing railway infrastructure resilience.