Chronic Kidney Disease (CKD) is a significant public health concern worldwide, with a substantial impact on patient outcomes and healthcare costs. Early identification of high-risk CKD patients is crucial for timely intervention and personalized treatment strategies. Machine learning techniques have emerged as powerful tools for predicting CKD risk and identifying patients at high risk of disease progression. This paper provides a comprehensive review of machine learning approaches employed in identifying high-risk CKD patients. We discuss various data sources, feature selection methods, and machine learning algorithms used in CKD risk prediction models. Furthermore, we explore the challenges and opportunities associated with applying machine learning in CKD prediction and highlight potential future research directions