Pneumonia, a severe and potentially fatal infectious disease, primarily impacts the lungs in humans. Its main culprit is often identified as Streptococcus pneumonia, a type of bacteria. According to the World Health Organization (WHO), Pneumonia causes many deaths in India, responsible for one out of every three reported cases. Creating an automated system to detect pneumonia holds immense potential for expediting the treatment process, especially in remote regions where access to medical expertise may be limited. With the remarkable success of deep learning algorithms, Convolutional Neural Networks (CNN) have gathered significant interest for their effectiveness in analyzing medical images and facilitating disease classification. The methodology employed in this study revolves around the execution of a CNN known as VGG19. This architecture is utilized to process X-ray images and carry out predictive analysis. To carry out the experiments, a diverse collection of chest X-ray images is employed, including both cases with pneumonia and cases without pneumonia. This dataset is utilized to train and test the CNN model. Our main discoveries highlight the impressive effectiveness of the recommended DL model in accurately predicting pneumonia. The VGG19 model, once trained, attained an extraordinary accuracy of 95.35% on the test dataset. Additionally, the model displayed a high sensitivity of 98.77%, demonstrating its proficiency in accurately identifying both positive and negative pneumonia cases. These findings strongly emphasize the capability of deep learning algorithms in assisting radiologists and clinicians by Detecting pneumonia at an early stage, enabling swift and targeted treatment intervention.