Chronic Kidney Disease (CKD) is a significant global health issue, affecting millions of individuals and leading to severe complications such as kidney failure and cardiovascular diseases. Early detection and accurate prediction of CKD progression are essential for timely intervention and improved patient outcomes. Machine Learning (ML) techniques have emerged as powerful tools in healthcare, offering predictive models that analyze large datasets to identify CKD risk factors, diagnose the disease at an early stage, and optimize treatment plans. This paper explores various ML algorithms, including logistic regression, decision trees, support vector machines, random forests, artificial neural networks, and deep learning models, in the context of CKD prediction. It evaluates their effectiveness based on metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Despite their potential, challenges such as data quality, model interpretability, and regulatory compliance must be addressed to ensure clinical applicability. Future directions include federated learning, explainable AI, hybrid models, and wearable-based monitoring for real-time risk assessment. The integration of ML into CKD management has the potential to revolutionize patient care, reducing mortality and healthcare costs while enhancing personalized treatment strategies.