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Pneumonia is a life-threatening respiratory disease that demands prompt and accurate diagnosis. Chest X-ray imaging is widely used to diagnose pneumonia; however, manual interpretation by radiologists is both time-consuming and subjective. In this paper, we propose a deep learning framework employing Convolutional Neural Networks (CNNs) to automate the detection of pneumonia from chest X-ray images. Our approach involves extensive data preprocessing, network architecture design, and hyperparameter tuning. Experimental evaluations on a publicly available dataset demonstrate that the proposed model achieves competitive accuracy compared to existing methods. Detailed analyses, including confusion matrices and performance metrics, are presented, along with discussions on limitations and future work.