Automated sports analytics using artificial intelligence (AI) and computer vision has gained significant attention in recent years. This project presents a tennis match analysis system that detects players, tracks ball movement, and extracts performance metrics using deep learning techniques. The system employs YOLO for real-time player and ball detection, along with CNNs for court keypoint extraction and perspective correction. By analyzing video frames, the system calculates shot speed, player movement speed, and shot counts, providing valuable insights into gameplay dynamics.The methodology involves video acquisition, frame extraction, preprocessing, and deep learning-based tracking. A rolling mean filter is applied to ball trajectory data to identify shot impact points and analyze rally patterns. Experimental results demonstrate the model’s effectiveness, achieving a detection accuracy of 92.3% (mAP) and reliable tracking of key game events. The extracted performance metrics offer valuable applications for coaches, analysts, and players, enhancing strategic decision-making and training efficiency.The proposed approach bridges the gap between traditional sports analysis and AI-based automation, paving the way for more advanced player performance evaluation and match strategy optimization.