Object tracking is a crucial component of computer vision that enhances numerous AI applications, including self-driving cars, surveillance, and sports analytics, by detecting and monitoring objects within video frames. The process involves complex algorithms that range from basic machine learning to sophisticated deep learning models, each offering unique benefits and serving various use cases. Object tracking can be divided into single object tracking (SOT) and multiple object tracking (MOT), with each approach presenting its own challenges and advantages. Popular algorithms like YOLO, DeepSORT, and MDNet utilize different methodologies to improve accuracy and inference time, which are the primary metrics for evaluating their effectiveness. These algorithms have revolutionized several industries, enabling advancements in autonomous vehicles, traffic monitoring, sports strategy development, and retail analytics. Despite their significant impact, challenges remain, such as handling diverse object types and varying conditions, requiring extensive datasets and computational resources for robust performance.