It is important to detect fractures timely so as to ensure long-term health. However, hairline fractures—these are small cracks in the bones and have no line of visible breakage—are very challenging to notice. Moreover, there is a scarcity of such fractured specific datasets which presents a big challenge for both manual and automated diagnostic systems. This study presents a novel method for improving the detection capability of hairline fractures using the stacking ensemble learning model. In order to enhance diagnosis accuracy, this method combines various advanced deep learning models including ResNet-50, VGG16, AlexNet, and Convolutional Neural Network (CNN). This ensemble model is thoroughly compared with these traditional models utilizing measures such as accuracy, precision, recall, and F1-score. The ensemble model by achieving an astounding 95.48% accuracy rate attests to its extraordinary performance in terms of detection rate and adaptability in many real-world scenarios. This development closes a major gap in current approaches and represents a major advancement in the field of medical diagnosis for hairline fractures.