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Pneumothorax, the presence of air in the pleural cavity, is a critical medical condition that can lead to lung collapse and severe respiratory distress. Early and accurate diagnosis is essential for timely intervention. Traditional methods, such as manual assessment of chest X-rays or CT scans by radiologists, are time-consuming, subjective, and prone to inter-observer variability. This project proposes an AI-driven approach for the automated segmentation of pneumothorax using deep learning techniques. By leveraging state-of-the-art Convolutional Neural Networks (CNNs) and U-Net-based architectures, the system can accurately delineate pneumothorax regions in chest X-ray and CT images. Preprocessing techniques such as image normalization, contrast enhancement, and noise reduction are applied to improve the quality of medical images before feeding them into the segmentation models. The proposed system is designed for real-time clinical use, enabling rapid and reliable pneumothorax detection. It can assist radiologists in diagnosis, treatment planning, and monitoring of lung recovery. Furthermore, the integration of transfer learning allows the model to generalize across different datasets, imaging modalities, and hospital settings. This automated segmentation framework has significant applications in critical care, emergency medicine, telemedicine, and hospital workflow optimization. By combining deep learning, medical image processing, and AI-based segmentation, this project provides a robust, scalable, and efficient solution for accurate pneumothorax identification and clinical decision support.