The X - ray images examinations is frequently used to identify a number of disorders, including pneumonia. Pneumonia is one of the most frequent infectious disease, producing huge morbidity and fatality rates. Roughly 15% of all child deaths occur because of pneumonia. This disease can be discovered by taking an X-ray scan of the chest of a patient. But, evaluating such X-ray scans directly demands a lot of time for processing. To overcome this difficulty, we suggested an algorithmic strategy for detecting sick areas in X-ray images to aid doctors in detecting pneumonia. The X-ray images of the chest are an effective component in the treatment of pneumonia, and many clinician decisions are based on it. As a result of their superiority in providing the finest solutions, healthcare systems are increasingly incorporating Machine Learning and deep learning into their operations. In this study, we proposed a Deep learning approach for pneumonia detection. As deep learning has the power to address complex issues, we choose deep learning-oriented pneumonia diagnosis. We employed a dataset from Kaggle and applied various transfer learning techniques resnet, efficient net, and VGG19 for pneumonia detection with X-ray chest pictures. PyTorch, a library for Python, is used to implement the proposed approach. The results suggest that our proposed approach exhibited a decent accuracy rate for pneumonia detections.