Acute Kidney Injury (AKI) is a common complication after surgery. Recognition of patients at risk of AKI at an earlier stage is a priority for researchers and health care providers. The objective of this study is to develop machine learning prediction models of acute kidney injury (AKI) in patients who undergo surgery. The dataset used in this study consists of in-hospital patients' data of five different cohorts coming from different major procedure types. This data was collected from the SunRiseClinical Manager (SCM) electronic medical records system that is used in the Calgary Zone, Alberta, Canada from 2008 to 2015 where the patients are >=18 years of age. Five classifiers were experimented with: support vector machine, random forest, logistic regression, k-nearest neighbors, and adaptive boosting. The area under the receiver operating characteristics curve (AUROC) ranged between 0.62-0.84 and sensitivity and specificity ranged between 0.81-0.83 and 0.43-0.85, respectively. Predictions from these models can facilitate early intervention in AKI treatment.