Pneumonia, a respiratory infection caused by the inflammation of air sacs due to viruses and bacteria, affects approximately 7% of the global population annually, with 4 million patients facing fatal risks. Early diagnosis is crucial, and typical symptoms include chest pain, shortness of breath, and cough. However, diagnosing pneumonia in children is challenging due to the low sensitivity of tests and weak clinical findings. Chest X-rays have become an important diagnostic tool, but the conventional approach involving manual examination by radiologists is time-consuming, subjective, and can vary in accuracy. To address this, the proposed model leverages machine learning (ML), specifically designed for image analysis, to automatically learn and extract relevant features from chest X-ray images. The dataset consists of annotated chest X-rays collected from diverse patient populations, including both pneumonia-positive and pneumonia-negative cases. This model holds significant implications for the medical field and patient care, as it can rapidly analyze large volumes of chest X-ray images and accurately detect pneumonia patterns with a high level of precision. This will enable healthcare professionals to prioritize urgent cases, expedite diagnosis, and promptly initiate appropriate treatments, leading to improved patient outcomes, reduced hospital stays, and optimized resource allocation within healthcare facilities.