Pneumonia is a major respiratory illness and continues to create a considerable healthcare challenge, particularly within developing countries such as India. The burden becomes even more critical in communities where access to specialist diagnostics is limited. Although chest X-ray imaging remains one of the most common investigation techniques, interpreting radiographs accurately requires trained radiologists who can differentiate subtle abnormalities in lung structures. In many semi-urban and rural healthcare facilities, there is often a shortage of such qualified experts, resulting in delayed diagnosis, inconsistent interpretation, and slower treatment initiation. To address this gap, this work presents a deep learning-enabled solution for automated pneumonia detection using chest X-ray scans. The proposed system utilizes Convolutional Neural Networks (CNNs), known for their strong capability in extracting visual patterns and learning hierarchical features from medical images. The model is trained to classify X-ray images into pneumonia-positive and normal categories with improved reliability compared to manual screening. Beyond model development, the system is deployed through a Flask-based web framework, allowing healthcare workers to upload X-ray images and receive instant diagnostic predictions through an intuitive interface. This solution aims to supplement clinical decision-making rather than replace medical professionals. By providing rapid evaluations, uniform assessments, and usability on low-resource computing devices, the system can serve as an assisting tool in hospitals, diagnostic centers, and telemedicine platforms. Ultimately, the goal is to make pneumonia diagnosis faster, more consistent, and widely accessible, especially in regions where expert radiological services are scarce. The integration of deep learning and web-based technology demonstrates the potential for AI-driven healthcare systems to enhance patient care and bridge healthcare accessibility gaps in underserved areas.