Pneumonia is still a major problem in world health, causing a lot of illness and death, particularly among the most susceptible people. For care and therapy to be successful, a prompt and precise diagnosis is essential. Recent years have seen encouraging outcomes in medical image processing tasks when using deep learning methods, including Convolutional Neural Networks (CNNs). Using deep convolutional neural networks (CNNs) and transfer learning models, we provide a machine learning application for detecting pneumonia in medical photographs.Our suggested system makes use of convolutional neural network (CNN) designs that have already been trained using massive natural picture datasets, including VGG, ResNet, and DenseNet. We hope that by training these algorithms on a collection of chest X-ray pictures, we may modify their characteristics to better diagnose pneumonia. Furthermore, we investigate data augmentation methods to improve the models' generalizability, especially in cases when there is a lack of labelled data.Standard measures including precision, sensitivity, specificity, and area under the curve of the receiver operating characteristic (AUC-ROC) are used to assess the performance of the created models on a heterogeneous dataset of chest X-ray images.