The advancement of self-driving vehicles underscores the importance of robust traffic sign recognition systems. Accurate interpretation of traffic signs is pivotal for passenger safety and efficient navigation. To address this, we propose a novel approach employing the MobileNet Architecture and YOLOv5 for traffic sign recognition. Leveraging MobileNet Architecture, we achieved remarkable performance with a Training Accuracy of 97.00% and Validation Accuracy of 98.00%.