Railway infrastructure is a cornerstone of global transportation systems, providing an efficient and economical means of moving freight and passengers. However, the increasing demands of higher train speeds, heavier axle loads, and growing traffic volumes, combined with exposure to harsh environmental conditions, have made railway components more susceptible to degradation. Among these, ballast deficiency is a critical issue that can lead to track instability, increased maintenance costs, and safety risks. Ballast, which forms the foundation of railway tracks, is subject to wear, fouling, and settlement over time, necessitating advanced monitoring techniques to ensure its integrity. This study explores the use of FBG sensors to monitor ballast deficiencies in railway tracks. A detailed numerical model was created using the finite element method (FEM) to analyse strain distribution in the rail under different ballast conditions. The model aimed to predict strain patterns linked to ballast deterioration and settlement. Experimental tests were carried out on a full-scale test rig with FBG sensors embedded in a controlled ballast environment, subjected to both moving and static loads from the bogie. The findings revealed a strong agreement between numerical simulations and experimental results, confirming the reliability of FBG sensors in identifying ballast deficiencies, particularly for sensors located near the ballast.