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Chronic Kidney Disease (CKD) is a major public health concern with rising prevalence. In Saudi Arabia, approximately 2 Billion Riyals are solely allocated for renal replacement therapy which is required for patients with advanced stages of CKD. Therefore, this study aims to decrease the number of patients and the expenses needed for treatment by preemptively diagnosing chronic kidney disease accurately using data mining and machine learning techniques. Data have been collected from a region that has never been explored before in literature. This study uses Saudi data retrieved from King Fahd University Hospital(KFUH) in Khobar to carry out the experiment. Experimental Results show that ANN, SVM, Naïve Bayes achieved a testing accuracy of 98.0% while k-NN has achieved an accuracy of 93.9%.