Chronic Kidney Disease (CKD) is a pressing global health concern, where early diagnosis and effective management are vital to prevent progression to end-stage renal failure. This review paper analyzes advancements in the prediction and classification of CKD and related kidney disorders through machine learning (ML) techniques. It explores a spectrum of methodologies, ranging from traditional statistical models to advanced deep learning approaches, assessing their effectiveness in enhancing diagnostic accuracy. A key contribution of this work is the proposal of a novel methodology and block diagram for integrating diverse data sources, including patient demographics, clinical measurements, and medical images, to improve predictive outcomes. The proposed system leverages Convolutional Neural Networks (CNNs) for image analysis and employs ensemble methods for feature integration, aiming to optimize predictive performance. The review also addresses significant limitations, such as data quality and feature selection challenges, while emphasizing the advantages of early detection and personalized treatment through advanced ML models. By identifying research gaps and suggesting future directions, this paper aims to foster the development of more effective algorithms and real-time monitoring systems for CKD and kidney disorder management, ultimately contributing to improved patient outcomes.