Chronic Kidney Disease (CKD) is a chronic type of disease with gradually impaired kidney functioning as its characteristic feature, whereas its global problem has become a critical issue of public health. The World health Organization (WHO) refers CKD to be one of the leading causes of morbidity and mortality globally and in low- and middle-income countries mostly missed at an early stage. This disease is usually asymptotic at the initial stages hence requiring effective and precise predicted procedures that identify CKD before graduating to end-stage renal failure. Machine Learning (ML) has become a very effective instrument in the field of medicine, especially in terms of disease prediction, in recent years because of its ability to practice sophisticated patterns in substantial volumes of data. ML algorithms Decision Trees, Support Vector Machines, Random Forest, Deep Learning architecture have been used to create predictive models that have potential to help identify personalities that are at risk of having CKD. Moreover, stratification methods, including demographic-, diseases- and lifestyle-related stratification, help improve the specificity and significance of such models because they allow them to be used to assess risks to the individual. Although ML has potential in the prediction of CKD, issues such as the standardization of data as well as management of imbalanced data, interpretability, and the incorporation of clinical validation need to be addressed. In addition, a possible integration of the new categorization models with the latest ML methods provides a way to new, more stable, more transparent, and clinically acceptable models. The given systematic review is aimed at a broad analysis of the available literature on the prediction of CKD and refers to the studies that integrate an approach to categorization and the novel machine learning strategies. The review compares the methodologies, performance measures, characteristics of the data and clinical implications to give a summative view which can be used in future research and practical implications.