Prediction of acute kidney injury (AKI) in critically ill patients allows prompt intervention that improves outcome. We aimed for external validation of two AKI prediction scores that can be bedside calculated. A prospective observational study included patients admitted to medical and surgical critical care units. Performance of two AKI prediction scores, Malhotra score and acute kidney injury prediction score (APS), was assessed for their ability to predict AKI. The best cutoff point for each score was determined by Youden index. Area under the receiving operation characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were used to assess performance of each score. Univariate and multivariate regression analyses were done to detect the predictability of AKI. Goodness-of-fit and kappa Cohen agreement tests were done to show whether the expected score results fit well and agree with the observed results. AKI prevalence was 37.6%. The best cutoff values were 5 and 4 for Malhotra score and APS, respectively. Area under the curve for Malhotra 5 was 0.712 and for APS 4 was 0.652 with nearly similar sensitivity and specificity. Regression analysis demonstrated that Malhotra 5 was the independent predictor of AKI. Goodness-of-fit test showed significant results denoting lack of fit between the scores and the actual results. Kappa test showed moderate agreement for Malhotra 5 and fair agreement for APS 4. Both scores showed moderate performance for AKI prediction. Malhotra 5 showed better performance compared to APS 4. Multicenter international study is warranted to develop a universal model that can predict AKI in critically ill patients.