<p>Extreme weather conditions can cause significant damage to transport infrastructure, including railways, and disrupt its services. In this context, rainfall-induced landslides have been extensively studied with respect to failure probability; however, predicting the probability of damage to railway infrastructure remains challenging due to inadequate documentation of historical damage records from past extreme events. To address such data gaps, a hybrid data approach is proposed to quantify the physical vulnerability of railway embankments to rainfall-induced landslides, accounting for multiple damage states. In this study, the volume of a landslide is considered an appropriate damage indicator, and cumulative rainfall is used to assess the impact of rainfall-induced landslides on a railway embankment. The proposed integrated method includes maximum likelihood and best-fit regression to derive the fragility function, and Monte Carlo simulation is employed to estimate uncertainty in soil capacity. Data from a real case scenario in the Sri Lankan railways is used to demonstrate the application of the proposed method. Key parameters influencing rainfall-induced slope failure are identified to predict the fragility of rainfall-induced landslides in natural slope railway embankments using cumulative rainfall, with particular reference to small-scale events where landslide volume is less than 1000 m³. The application can be extended to assess the vulnerability of slope structures and earthworks and, ultimately, to support the development of resilient transportation infrastructure systems, provided that standardized data documentation and appropriate local data representation are adopted.</p>