Acute kidney injury (AKI) is a common disease that has a variable incidence from 20% to 70% in critical ill patients [1]. AKI is defined as an abrupt reduction of kidney functionality which may have critical consequences for patients. One of the major problems in treating AKI is the difficulty in identifying it on time. Studies and researches confirm that clinicians could carry out compensatory actions if they could identify the onset of AKI in advance; complications could be reduced and complete healing could be possible. The current work has as primary objective to develop a machine learning model that can support clinicians decisions and be able to provide a continuous AKI risk score for patients admitted to any intensive care units (ICU) .