This systematic literature review (SLR) examines the application of the YOLO (You Only Look Once) object detection algorithm in traffic sign detection and recognition systems. The study focuses on five key aspects: applications, datasets, metrics, hardware, and challenges. The review covers 115 primary studies published between 2016 and 2022, identifying three main applications: road safety, Advanced Driver Assistance Systems (ADAS), and autonomous driving. The most commonly used datasets include the German Traffic Sign Detection Benchmark (GTSDB), Tsinghua Tencent 100K (TT100K), and Chinese Traffic Sign Datasets (CTSDB and CCTSDB). Performance metrics such as frames per second (FPS), accuracy (ACC), precision, recall, F1 score, mean average precision (mAP), intersection over union (IoU), and average precision (AP) are frequently used to evaluate the systems. Popular desktop data processing hardware includes Nvidia RTX 2080 and Titan Tesla V100, while embedded or mobile GPU platforms like Jetson Xavier NX are also prevalent. Seven significant challenges in real-world road conditions are identified, including variations in lighting, adverse weather, partial occlusions, and suboptimal image quality. The SLR provides insights into the current state of YOLO-based traffic sign detection and recognition systems, highlighting areas for future research and improvement.This systematic literature review (SLR) examines the application of the YOLO (You Only Look Once) object detection algorithm in traffic sign detection and recognition systems. The study focuses on five key aspects: applications, datasets, metrics, hardware, and challenges. The review covers 115 primary studies published between 2016 and 2022, identifying three main applications: road safety, Advanced Driver Assistance Systems (ADAS), and autonomous driving. The most commonly used datasets include the German Traffic Sign Detection Benchmark (GTSDB), Tsinghua Tencent 100K (TT100K), and Chinese Traffic Sign Datasets (CTSDB and CCTSDB). Performance metrics such as frames per second (FPS), accuracy (ACC), precision, recall, F1 score, mean average precision (mAP), intersection over union (IoU), and average precision (AP) are frequently used to evaluate the systems. Popular desktop data processing hardware includes Nvidia RTX 2080 and Titan Tesla V100, while embedded or mobile GPU platforms like Jetson Xavier NX are also prevalent. Seven significant challenges in real-world road conditions are identified, including variations in lighting, adverse weather, partial occlusions, and suboptimal image quality. The SLR provides insights into the current state of YOLO-based traffic sign detection and recognition systems, highlighting areas for future research and improvement.