Among all the gynecological cancers, cervical cancer can be regarded as the second most prevalent cancer type in less developed areas. Nowadays, one important screening method in the early diagnosis of this type of cancer is the Pap-Smear test and among all the methods, the Pap test is the one which is extensively applied in cervical cancer diagnosis. Machine Learning has the potential to provide accurate prognosis by conducting classification, prediction and estimation based on the images. The purpose of the current research is to classify Pap-Smear images by different Machine Algorithm Methods to achieve high prediction rate. The Ensemble technique combines different machine learning techniques: K-Nearest Neighbor, Support Vector Machine, and Multi-Layer Perceptron. The last mentioned technique achieved the highest accuracy of 97.83%. In sum, machine learning has the potential to achieve high diagnosis accuracy, in an efficient manner.