Machine learning (ML) is increasingly applied in chronic kidney disease (CKD) to improve diagnosis, prognosis, and treatment. This review discusses the current status and future prospects of ML in CKD. ML, combining statistics and computer science, enables computers to extract insights from large datasets, aiding in the development of statistical models and data interpretation. The integration of ML into clinical algorithms aims to enhance efficiency and promote its adoption as a standard approach in nephrology. Collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, contributing to precision diagnostics and personalized medicine in CKD. ML methods include linear regression, logistic regression, decision trees, random forests, k-nearest neighbor (k-NN), support vector machines (SVM), and artificial neural networks (ANN). These models have shown promise in predicting CKD progression, estimating glomerular filtration rate (eGFR), and identifying risk factors for kidney disease. For example, logistic regression models have been used to differentiate diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD), achieving high accuracy. Random forest models have demonstrated superior performance in predicting kidney failure progression, with high sensitivity and specificity. In IgA nephropathy (IgAN), ML models have been used to predict kidney failure progression, with some models achieving high predictive accuracy. In hemodialysis, ML has been applied to predict patient mortality, manage complications, and optimize dialysis prescriptions. For instance, a random forest model was developed to predict mortality risk within 30, 90, 180, and 365 days after starting dialysis, showing good predictive reliability. The future of ML in CKD includes the development of non-invasive diagnostic tools, improved risk prediction models, and enhanced personalized treatment strategies. However, challenges remain, including data drift, model interpretability, and ethical considerations. Ensuring fairness, transparency, and patient privacy is crucial for the responsible application of ML in nephrology. Collaborative efforts between clinicians and data scientists are essential for advancing ML applications in CKD, leading to more accurate and effective healthcare solutions.Machine learning (ML) is increasingly applied in chronic kidney disease (CKD) to improve diagnosis, prognosis, and treatment. This review discusses the current status and future prospects of ML in CKD. ML, combining statistics and computer science, enables computers to extract insights from large datasets, aiding in the development of statistical models and data interpretation. The integration of ML into clinical algorithms aims to enhance efficiency and promote its adoption as a standard approach in nephrology. Collaboration between clinicians and data scientists is essential for defining data-sharing and usage policies, contributing to precision diagnostics and personalized medicine in CKD. ML methods include linear regression, logistic regression, decision trees, random forests, k-nearest neighbor (k-NN), support vector machines (SVM), and artificial neural networks (ANN). These models have shown promise in predicting CKD progression, estimating glomerular filtration rate (eGFR), and identifying risk factors for kidney disease. For example, logistic regression models have been used to differentiate diabetic kidney disease (DKD) from non-diabetic kidney disease (NDKD), achieving high accuracy. Random forest models have demonstrated superior performance in predicting kidney failure progression, with high sensitivity and specificity. In IgA nephropathy (IgAN), ML models have been used to predict kidney failure progression, with some models achieving high predictive accuracy. In hemodialysis, ML has been applied to predict patient mortality, manage complications, and optimize dialysis prescriptions. For instance, a random forest model was developed to predict mortality risk within 30, 90, 180, and 365 days after starting dialysis, showing good predictive reliability. The future of ML in CKD includes the development of non-invasive diagnostic tools, improved risk prediction models, and enhanced personalized treatment strategies. However, challenges remain, including data drift, model interpretability, and ethical considerations. Ensuring fairness, transparency, and patient privacy is crucial for the responsible application of ML in nephrology. Collaborative efforts between clinicians and data scientists are essential for advancing ML applications in CKD, leading to more accurate and effective healthcare solutions.