Railway signaling systems play a critical role in ensuring the safety and efficiency of train operations. The verification and validation of these systems are essen- tial to prevent accidents and ensure reliable performance. This research paper proposes a novel approach to revolutionize railway signaling verification and val- idation using Convolutional Neural Networks (CNNs). The proposed framework, CNN-RSVV, leverages the power of CNNs for automatic feature extraction and pattern recognition from signaling data. The framework’s effectiveness is demon- strated through simulation-based testing and real-world deployment, showcasing its potential to enhance safety and reduce human-dependent error in railway oper- ations. The study contributes to the field of safety-critical systems by introducing an innovative solution that combines machine learning with railway signaling domain knowledge.