Road sign detection is an important part of intelligent transportation systems. Therefore, the navigation process of any autonomous vehicle will be safe and fruitfully conducted. In this paper, it has been described that a powerful model was developed, which is CNN-based; it was implemented by the Keras and TensorFlow libraries for the recognition and categorization of 43 different classes of road signs. The dataset used here is publicly available on Kaggle. It presents a sequential model architecture, comprising three blocks of convolutions and fully connected layers. Therefore, this model places great emphasis on both accuracy and efficiency in inference. This system also includes a variety of other functionalities—in fact, it is one of the most talked-about speech-to-text applications because it can present the detected signs translated into spoken words in English. This is a multilingual audio aid, thus adaptable in various languages around the world, and hence better accessible. This system, therefore, aims at the dynamic assignment of labels and voice outputs to be a virtual assistant for the visually impaired in the promotion of safe mobility in complex environments. Scalability and usability of the system in vehicle automation, assistive technologies, international traffic systems, and performance metrics while real-time testing and comparative analysis underscore model effectiveness. Their model bridges the gaps between accessibility and automation by the integration of advanced machine learning techniques with practical features. This proposed solution is well-directed to deliver viability across the globe for road safety and navigation.