The accurate diagnosis of kidney anomalies, such as cysts, stones, tumors, and normal tissues, is critical for early intervention and effective treatment. In this research, we propose a novel deep learning-based approach for renal image classification to enhance the precision and efficiency of diagnostic procedures. The conventional systems used for diagnosing kidney anomalies primarily rely on manual interpretation of radiological images, which is time-consuming, subjective, and prone to human errors. Furthermore, these traditional methods struggle with the increasing volume of medical images, making it essential to develop an automated system. Our proposed system leverages deep neural networks, specifically convolutional neural networks (CNNs), to automatically classify renal images into different categories, providing rapid and accurate results. The drawbacks of conventional systems, such as inter-observer variability, limited scalability, and the potential for misdiagnosis, can be significantly mitigated through our deep learning approach. We trained our model on a large dataset of annotated renal images, encompassing various anomalies and normal tissues, to ensure robust performance. Preliminary results indicate high accuracy, sensitivity, and specificity in identifying kidney anomalies, making our proposed system a promising tool for improving the diagnostic process in the field of nephrology.