Kidney diseases represent a growing global health concern, necessitating early detection and precise diagnosis to improve patient outcomes. Recent advancements in deep learning and image processing have revolutionized medical imaging, providing robust tools for disease identification and classification. The integration of deep learning methods in kidney health diagnostics is examined in this survey, with an emphasis on image processing applications including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound. It examines cutting-edge methods, such as attention processes, transfer learning, and convolutional neural networks (CNNs), emphasizing how well they can identify kidney abnormalities such stones, cysts, and chronic kidney disease (CKD). Challenges such as data scarcity, model interpretability, and real-time deployment are discussed alongside strategies for overcoming them, including data augmentation and federated learning. With insights into new patterns and potential paths forward, the survey continues by highlighting how AI-powered technologies could improve kidney disease management globally, save healthcare costs, and increase diagnostic accuracy.