<p>Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures.</p>