Pneumonia ranks among the world’s major causes of mortality and is the greatest cause of death for young children. It is an infectious condition that can be fatal, affects one or both lungs and is brought on by harmful bacteria. An accurate and timely diagnosis is essential for managing and treating patients effectively. Radiotherapists with specialized training are needed to assess chest X-rays to diagnose pneumonia. Therefore, creating an automated approach to identify pneumonia would be advantageous to treat the illness, especially in isolated locations quickly. This project offers a novel method for improving chest X-ray image quality, which is then used in conjunction with machine learning approaches to increase the detection accuracy of pneumonia. Subtle details in X-rays can be seen much better using picture-enhancing techniques including sharpening, contrast stretching, and histogram equalization. A VGG net and a convolutional neural network (CNN) model that can accurately diagnose pneumonia is trained using this augmented image dataset. By bridging the gap between conventional X-ray imaging and sophisticated machine learning, the initiative offers a viable approach to the early and accurate detection of pneumonia. Early disease identification is greatly aided by medical imaging, and chest X-rays are a frequent method of identifying lung disorders like pneumonia. This project offers a novel method for improving chest X-ray image quality, which is then used in conjunction with machine learning approaches to increase the detection accuracy of pneumonia. Subtle details in X-rays can be seen much better using picture-enhancing techniques including sharpening, contrast stretching, and histogram equalization. A Convolutional Neural Network (CNN) model that can accurately diagnose pneumonia is trained using this augmented image dataset. By bridging the gap between conventional X-ray imaging and sophisticated machine learning, the initiative offers a viable approach to the early and accurate detection of pneumonia.