This paper presents the development and evaluation of a robust system that utilizes the You Only Look Once (YOLO) object detection algorithm to detect and analyze run-out scenarios in real-time. YOLO-based systems have the potential to automate intricate decision-making processes in sports, minimize human error, and enhance the overall fairness and efficiency of the game. The system is trained and tested on a diverse dataset comprising thousands of images and video frames, capturing various match conditions, player positions, and lighting scenarios. The proposed system demonstrates exceptional performance in identifying key elements such as bat, ball, pitch line, and bail-off wicket, enabling precise determination of run-out events. The experimental results reveal outstanding accuracy, with the system achieving high precision and recall rates across multiple test cases. We have tested our proposed algorithm with more than 100 images/videos. The accuracy, precision, recall, and F1 values shows the robustness of the algorithm. The accuracy can be reported as \(95\%\) . The system’s ability to process real-time video feeds and deliver instant decisions makes it a valuable tool for umpires, broadcasters, and cricket analysts.