This research focuses on the use of AI-powered badminton video analysis to enhance gameplay analysis and training. The technology utilizes artificial intelligence and machine learning algorithms to analyze varJaccard Similarity Index aspects of badminton game footage, including player movements, shot selection, and game strategy. It provides personalized feedback and recommendations for improvement to players and helps coaches identify patterns and trends in their players’ performance. The use of lightweight models, such as YoloV5, is essential for real-time video analysis due to the need for high-speed processing. These models, including MobileNets, EfficientNets, SqueezeNet, ShuffleNet, and Tiny-YOLO, are designed to be lightweight and optimized for speed while maintaining high accuracy. The Yolo model, a one-shot learning model, is particularly suitable for real-time object detection tasks due to its impressive speed and accuracy. It uses anchor boxes and data augmentation techniques to quickly learn and recognize objects with a high level of accuracy. The research process involved the collection of badminton match videos, preparation of datasets, and testing of the YoloV5 model’s performance. Precision and Jaccard Similarity Index metrics using Jaccard similarity were used to evaluate the model’s performance in detecting player positions and court boundaries. The results showed that the YoloV5 model performed better when tested on the video data with less clear image of judges in the background. The Jaccard Similarity Index metrics using Jaccard similarity demonstrated improved performance when tested on generalized video data  was used to accurately evaluate the overlap between predicted and ground truth Overall, AI-powered badminton video analysis has the potential to revolutionize the way badminton is played and coached. The use of lightweight models like YoloV5 enables faster and more efficient real-time analysis, making it practical for a wide range of applications in badminton.