The framework that includes data collection, data preprocessing, features extraction, features representation, and classifier testing for predicting the terroristterrorist attacks. In 2015 M. H. Khorshid et al.[16]. proposed a supervised standard, ensemble, and hybrid machine learning classification algorithms. .inIn addition, they introduced models for prediction of the terrorist groups which are responsible offor terrorist attacks with in the Middle East and geographical region from the year 2009 up to 2013,. To achieve the goal of their research;, two different experiments arewere conducted on the used data, as well as using List-wisea list-wise deletion approach to handle the missing data and provide a detailed comparative study of the used classification algorithms between different 10 different classifiers ; standard. Standard classification algorithms, hybrid classifiers, ensemble classifiers, and ensemble hybrid classifiers were applied using WEKA. The overall results showed that hybrid machine learning classifiers looklooked good compared to their single classifiers and provedprovided an obvious improvement in predictive accuracy over some standard comprehensible and ensemble methods. The overall performance of the different types of classifiers used proved that hybrid machine learning classifiers perform accurateaccurately.