Pneumonia is a lung ailment caused by bacterial or viral infections marked by swelling and trouble breathing deeply. Chest X-rays are a typical way to diagnose, which is so important. This study focuses on the application of deep learning techniques for pneumonia detection using chest X-ray images. The dataset has images of chest X-rays arranged based on whether they are pneumonia cases or not, and these categories are further split into train, test, and validation folders. Providing a dataset comprising 5,863 JPG format X-rays which is 4,273 Pneumonia and 1,583 Normal, the dataset is a worthy resource for training and verifying machine learning models in distinguishing pneumonia from a normal X-ray. The methodology which takes place in training the deep learning model is a progressive process, starting with preprocessing steps and executing morphological operations, and histogram equalization. Transfer learning comes into play by using pre-trained models that have been made from various existing architectures such as MobileNetV2, DenseNet201, VGG19, and Xception, which are used to extract high-level features from the images, either alone or in a hybrid model that integrates features from multiple models. The models performed adequately, with MobileNetV2 achieving the highest accuracy of 97.43%, followed closely by DenseNet201 at 96.57% and Xception at 95.87%. VGG19 recorded the lowest accuracy at 92.39%. Combining MobileNetV2 and DenseNet201 into a hybrid model notably improved accuracy to 99.10%.