YOLO (You Only Look Once) is a real-time object detection algorithm that revolutionized the field by identifying and classifying objects in a single pass. Unlike traditional methods that involve separate steps for identification and classification, YOLO uses a single convolutional neural network (CNN) to divide an image into a grid, with each cell predicting bounding boxes and class probabilities. This approach, along with advancements through various versions from YOLOv1 to YOLOv9, emphasizes speed and accuracy, making it suitable for real-time applications across diverse fields such as healthcare, agriculture, security, and autonomous vehicles. YOLO's evolution has seen the introduction of multi-scale detection, advanced loss functions, and efficient backbone architectures to enhance precision and computational efficiency. Performance metrics like Intersection over Union (IoU) and Average Precision (AP) are used to evaluate these models, with the latest versions incorporating innovative techniques such as Programmable Gradient Information to maintain high accuracy. With open-source availability and applications in real-time scenarios, YOLO remains integral to modern computer vision tasks.