The segmentation of kidneys is a challenging task in medical image analysis, particularly for early diagnosis and treatment of renal disorders. Having a clear, well-defined outline of kidney structures from computed tomography (CT) and magnetic resonance imaging (MRI) helps doctors tremendously with diagnosis, surgical planning, and beyond, as well as with monitoring disease progression. But there are many problems, such as unevenly outlined boundaries, low image contrast, mottled patterns, and normal anatomical variations that occur from one patient to another, which all decrease the efficiency of the most common segmentation techniques. Therefore, to circumvent these constraints, this study proposes a framework, called Shape-Prior Learning, for kidney segmentation with deep networks enhanced with SOCAE. In principle, the concept is to integrate a convolutional auto-encoder based on shape, SOCAE, and a deep learning architecture to retain anatomical consistency and allow the system to learn structural cues and/or spatial characteristics of the kidneys. The pipeline comprises image pre-processing, feature extraction, shape-prior learning, and, at its end, the segmentation stage. The primary goal of the SOCAE part is to obtain the local texture information and additional global kidney shape representations. Therefore, the network can achieve higher segmentation accuracy and fewer boundary errors, such as misclassifications. Including shape-prior constraints in a deep network greatly reduces shape invariance in addressing the challenging kidney region while preserving edges. Furthermore, features are enhanced and normalized during training to ensure the model remains robust and can be applied to different medical image databases. Finally, experimental validation was performed on kidney benchmark image datasets using different performance metrics, including Accuracy, Precision, Recall, Dice Similarity Coefficient, and F1-Score. Comparing the SOCAE-enhanced deep network to the CNN and the U-Net-based CNN, respectively, in an almost consistent manner, it can be concluded that the proposed network achieved the highest accuracy. For accuracy, the model has a high accuracy rate of 0.9882, precision of 0.9814, recall of 0.9776, and F1-Score of 0.9795. The segmentations are consistent. The Precision improvement indicates fewer false-positive segmentation areas, and the overall higher F1 Score suggests a fairly good balance between Precision and Recall. Overall, these outcomes show the proposed Shape-Prior Learning approach meaningfully boosts kidney segmentation, and it offers a practical method for automated medical image processing, helping more intelligent clinical decision-making systems as well as computer-aided diagnosis tasks.