This paper presents a comprehensive AI-powered football analysis system that employs the YOLO (You Only Look Once) detection framework to achieve real-time identification and tracking of players, balls, and referees in football match videos. The system integrates computer vision techniques, including team classification through K-means clustering, optical flow for camera motion compensation, and homography transformation for perspective correction. Our implementation achieved remarkable performance metrics with 82.2% mAP50, 90.2% precision, and 77.0% recall across all object classes. The system successfully processes raw match footage to generate automated analytics, including player tracking, speed calculation, distance metrics, and possession statistics, offering an economical substitute for expensive GPS-based tracking systems used in professional sports.