In current times, novel Coronavirus Disease-19 (COVID-19) has become one of the world’s biggest challenges. The Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) which causes this disease results in high mortality and morbidity rates globally. It has been revealed from the study that patients infected with COVID-19 exhibit different radiographic visual characteristics along with dyspnea, fatigue, dry cough, fever, etc. In this account, Chest X-Ray (CXR) is considered as one of the most significant, non-invasive clinical methods that assist in detecting the visual responses that are related to SARS-CoV-2 infection. However, the radiologist’s and experts’ minimal availability and experts’ ability to interpret the CXR images and subtle images of the disease serve as the prominent hurdles in manual diagnosis. This study presents an automatic coronavirus disease-19 screening ensemble model that utilizes radiomic texture descriptors extracted from CXR images to identify normal, infected, and suspected coronavirus disease-19 patients. The ensemble model utilizes a majority vote-based classifier ensemble of two benchmark supervised classification algorithms. The training and testing of the proposed model are performed using the two datasets. The ensemble model can significantly improve prediction performance with 94% accuracy, while the precision and recall rate of detecting coronavirus disease-19 are both 93%.