This paper represents the integration of XAI based on visual techniques with deep learning in recognizing traffic signs using the German Traffic Sign Recognition Benchmark. So far, deep learning models for recognizing and classifying traffic signs have been very successful and are currently playing the most crucial role in monitoring transportation infrastructures. In this context, however, black-box models increase severe concerns about transparency in trust and safety–critical applications. This work identifies such challenges using visual XAI techniques, namely Grad-CAM and saliency maps, to interpret profound learning predictions. Its methodology is based on training a neural network on the GTSRB dataset, the CNN, and then generating visual explanations to point out which aspects of the model it focuses on during classification. The results show that visual explanations improve interpretability and insecurities about model decision-making, which makes the system more reliable for real-world deployment. These findings will underscore the importance of model transparency in critical infrastructure and open the way for modeling safer and more accountable AI-driven solutions in transportation monitoring. Future research directions include additional XAI techniques and an extension of this approach to other infrastructure domains.