Early classification of the Coronavirus disease (COVID-19) is necessary to control its rapid spread and save patients' lives. The fast spread of COVID-19 has increased the diagnostic encumbrance of radiologists. Therefore, clinicians need to quickly assess if a patient has COVID-19 or not. Artificial Intelligence (AI) has shown promising results in healthcare. So, this paper proposed a computer-aided intelligence model that can identify positive COVID-19 cases. It presented the pipeline of medicinal imaging and examination methods involved in COVID-19 image acquirement, segmentation, and diagnosis, using Computed Tomography (CT) images. This paper introduced two effective models for single machine learning (SML) and ensemble machine learning (EML) with 10-fold cross validation, to detect cases of COVID-19.The first classification model (SML) was applied with different algorithms, such as Decision Tree (DT), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). Results showed that the performance of the SVM surpassed other classifiers with a 98.85 % accuracy. The second classification model (EML) was applied with several algorithms, such as Random Forest (RF), Voting, and Bagging, to increase its accuracy up to 99.60%, especially using the Bagging classifier. Finally, the results of the two proposed models showed better performance compared with other recent studies. However, the EML showed an even better performance than SML and is recommended for use in real-time.