Accurately detecting the ball in field hockey videos is crucial for various applications such as player tracking, tactical analysis, and performance evaluation. This paper presents a detailed method for accurate ball detection using the YOLOv8 algorithm, which is renowned for its accuracy and real-time object detection capabilities. The proposed approach involves training the YOLOv8 model on a specialized dataset comprising annotated field hockey videos to enable precise ball identification and localization. Experimental evaluation using comprehensive metrics, including precision, recall, and mean Average Precision (mAP) demonstrates the effectiveness of the proposed method, showcasing high levels of accuracy and efficiency. By automating the ball detection process, this approach significantly reduces manual effort in field hockey video analysis and opens opportunities for advanced analytics, providing deeper insights into player behavior, strategic patterns, and overall game dynamics. The proposed method empowers researchers, coaches, and analysts to gain a comprehensive understanding of field hockey matches and make data-driven decisions to enhance team performance.