Kidney disease is a significant health issue that leads to a high number of deaths worldwide. Accurate and timely detection of kidney diseases, including cysts, stones, and tumours, is critical for effective treatment and patient outcomes. Deep learning methodologies, specifically transfer learning, have recently been widely used in medical image analysis. This study comprehensively evaluates seven transfer learning models—Xception, DenseNet201, MobileNet, InceptionV3, VGG16, ResNet50, and EfficientNetB0—for multiclass kidney disease classification using CT scan images. The models are assessed based on key performance metrics such as accuracy, loss, precision, recall, F1-score, and AUC. Results indicate that ResNet50 delivers the best overall performance, making it a promising model for clinical applications. By comparing these state-of-the-art models, this research provides valuable insights into the most effective transfer learning approaches for kidney disease detection, paving the way for robust computer-aided diagnostic systems.