This paper outlines a methodology for Explainable AI (XAI) leveraging convolutional neural networks to improve the interpretability of machine learning models, specifically for diagnosing pneumonia from X-ray images. By incorporating Local Interpretable Model-agnostic Explanations (LIME), our approach ensures model transparency while maintaining accuracy. This enhancement boosts diagnostic reliability and aids decision-making in clinical environments, addressing the critical need for interpretable AI in healthcare. Our approach not only aims to reduce pneumonia-related morbidity and mortality through early diagnosis but also optimizes the utilization of healthcare resources and fosters trust in AI technologies among medical professionals.