Accurate classification of medical images, particularly CT scans, plays a crucial role in diagnosing and detecting renal anomalies such as tumors, stones, and cysts. This study explores the effectiveness of deep learning techniques, specifically the VGG16-based Convolutional Neural Network (CNN), in automating kidney disease diagnosis. The research leverages a large dataset comprising thousands of kidney CT images to train and evaluate the model’s performance based on key metrics such as accuracy, precision, recall, and F1-score. The proposed approach demonstrates high classification accuracy, with sensitivity and specificity exceeding 99%, indicating its potential for real-world clinical applications. To enhance the model’s robustness, various data preprocessing techniques—including image normalization, contrast enhancement, and noise reduction—are employed to optimize image quality. Furthermore, transfer learning is utilized to fine-tune the VGG16 model, allowing it to extract deep hierarchical features from medical images while reducing computational costs. The study also emphasizes the importance of incorporating explainable AI techniques, such as Grad-CAM, to improve model interpretability and facilitate clinician trust in AI-driven diagnostics. Overall, this research highlights the transformative potential of AI in medical imaging, particularly in automating kidney disease detection. By leveraging deep learning for early diagnosis, this study contributes to improving patient outcomes and optimizing clinical workflows. Continued advancements in AI-based medical diagnostics could significantly enhance precision medicine and facilitate more efficient, accessible, and reliable healthcare solutions.