This research work provides the comparison of performance of Artificial Neural Network (ANN), Support vector machine (SVM) and K-Nearest-Neighbour (KNN) models for cardiac ischemia classification. The novelty of this work is to develop an ANN, SVM and KNN model for classification of cardiac ischemia based on morphological changes in ECG signals. ST-depression, ST-elevation and T-wave inversion changes in ECG signal are the early indicators of cardiac ischemia. Ischemic beats are classified from normal beats using morphological features extracted from ECG beat. The proposed ANN, SVM and KNN models receive the morphological features extracted from preprocessed ECG beat. The performance of all models are compared and validated on physiobank database in terms of accuracy, sensitivity and specificity. The results of this study exhibits that the proposed ANN based model has great potential than that of SVM and KNN classifiers for classification of cardiac ischemia