In recent years, deep learning has become increasingly applicable in different fields, especially considering the increasing amount of data available in the medical field. Medical data plays a crucial role in the field of artificial intelligence. With millions of medical records being collected, we can comprehensively develop a predictive system for disease susceptibility, incidence rates, and more. This system can help patients reduce treatment time and gain a clearer understanding of their health conditions. Both deep learning and machine learning are increasingly used in healthcare. Medical imaging data, such as X-ray images, is extensively used in diagnostic tasks related to pneumonia. Deep learning models leverage a large volume of X-ray images and, through the extraction of image features, can distinguish whether a patient has pneumonia or not. In this article, we explain how advanced deep learning models, in particular Convolutional Neural Networks (CNNs), can be used to analyze chest X-ray images to support accurate diagnosis. A Convolutional Neural Network (CNN) model has been developed to tackle the classification challenge of determining the presence of pneumonia in chest X-rays. This model utilizes a dataset comprising both normal chest X-rays and those indicating viral pneumonia. In this study, the VGG16 and ResNet50 models are experimentally implemented, and their accuracies are rigorously evaluated.