The growing intricacy of traffic systems and the demand for improved road safety have prompted the advancement of sophisticated Traffic Sign Recognition (TSR) technologies. With an emphasis on the application of machine learning and deep learning techniques for precise traffic sign recognition and classification, this survey article examines cutting-edge approaches and creative solutions in TSR. It examines a number of strategies, emphasizing their efficacy in a range of settings and real-time applications, such as convolutional neural networks (CNNs), hybrid algorithms, and Internet of Things-based systems. By analyzing developments in robust algorithmic design and data augmentation approaches, important issues, including managing occlusions, fluctuating weather, and different datasets, are addressed. This study also investigates how TSR systems can be integrated with driver warning systems, adaptive cruise controls, and autonomous cars, highlighting how they can improve road safety and navigation effectiveness. This survey offers a thorough framework for further research by combining data from the body of existing literature, providing insights into enhancing the accuracy, scalability, and practicality of TSR systems.