The application of deep learning in medical imaging has significantly advanced the capabilities of spine classification and reconstruction. This project delves into the utilization of state-of-the-art deep learning algorithms to enhance the accuracy and efficiency of spinal image analysis. By leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), this study aims to accurately classify various spinal conditions and reconstruct detailed three-dimensional (3D) models from medical images, including X-rays, CT scans, and MRIs. The methodology begins with a comprehensive preprocessing phase, involving normalization and data augmentation to ensure a robust and diverse training dataset. For the classification task, CNNs are employed due to their proficiency in recognizing complex patterns within medical images. These networks are trained to identify and categorize a range of spinal abnormalities, such as scoliosis, herniated discs, and vertebral fractures. The classification model is evaluated using metrics like accuracy, precision, recall, and F1-score, demonstrating superior performance compared to traditional diagnostic methods. In the reconstruction phase, GANs are utilized to generate high-fidelity 3D models of the spine. The GAN architecture includes a generator network that creates 3D reconstructions and a discriminator network that evaluates the realism of these reconstructions. The training process involves iterative improvements through adversarial learning, resulting in highly accurate and detailed 3D models. These models provide invaluable insights into the spinal anatomy and pathology, facilitating improved visualization for diagnostic and surgical planning purposes. The experimental results highlight the effectiveness of deep learning models in both spine classification and reconstruction. The CNN-based classification model achieves high diagnostic accuracy, significantly reducing the rate of misdiagnosis. The GAN-based reconstruction model produces detailed and realistic 3D spinal models, offering enhanced visualization capabilities for clinical use. This project underscores the transformative potential of deep learning in spinal healthcare, providing clinicians with powerful tools for accurate diagnosis and precise surgical planning. The integration of these advanced technologies into clinical workflows promises to reduce diagnostic errors, streamline surgical procedures, and ultimately improve patient outcomes. Future work will focus on refining these models, incorporating additional medical imaging modalities, and expanding the application scope to other areas of medical imaging. By advancing the state of spinal image analysis through deep learning, this project aims to set a new standard in medical diagnostics and treatment planning, fostering better healthcare outcomes and advancing the field of medical imaging technology.