Chronic kidney disease (CKD) is a significant global public health issue affecting over 10% of the world's population. It is projected to become the 5th leading cause of death by 2040. CKD poses a burden on individuals, healthcare systems, and society due to increased hospitalizations, reduced productivity, morbidity, and premature mortality. Hypertension, glomerulonephritis, autosomal dominant polycystic kidney disease, and diabetic renal disease are common causes of CKD. However, many cases of CKD develop for unknown reasons. Early detection of CKD is crucial for appropriate preventive measures. Machine learning has been successfully employed in the medical field for detecting various diseases, and in this study, we propose to use machine learning algorithms such as logistic regression, K Nearest Neighbors, and decision trees to predict the presence of chronic kidney disease. This paper discusses related works, provides background information, and outlines the system design. Furthermore, the economic, social, political, and health impacts of CKD are examined. Ethical and professional responsibilities are discussed, along with the tools used in the study. Finally, the results are presented, and conclusions are drawn. The development of accurate prediction models for CKD can significantly contribute to early intervention, better management, and improved outcomes for individuals affected by this debilitating condition.