Pneumonia is one of the most dangerous respiratory diseases affecting children and contributes significantly to increased mortality rates in this age group. Traditional diagnosis relies on X-ray images, which require precise medical expertise, potentially impacting the speed and accuracy of diagnosis. This research aims to develop an intelligent system that utilizes artificial intelligence and deep learning techniques to analyze children's X-ray images and accurately diagnose pneumonia. A convolutional neural network (CNN) model was trained on data containing images of infected and healthy children. The results demonstrated high diagnostic accuracy, in some cases surpassing the performance of human experts.This system represents an effective tool for accelerating diagnostic processes, reducing error rates, and thus improving the quality of healthcare and alleviating the burden on medical staff. A professional graphical interface in Arabic was used to facilitate user interaction within the medical environment. An SQLite database was also integrated to store patient data and previous diagnoses in an organized and secure manner. The system operates offline, making it ideal for clinics and areas with limited infrastructure. The results have proven the feasibility of adopting this type of system as a tool to assist in immediate medical diagnosis. The project integrates programming, artificial intelligence, and healthcare applications seamlessly. This solution represents a significant step forward in the use of technology to serve children's health and the community. It can also be expanded in the future to include other diseases using similar technologies............... Keywords:............... CNN, Diagnosing pneumonia, children.