Pneumonia is a leading cause of morbidity and mortality globally, presenting challenges for early diagnosis and treatment. Accurate and efficient diagnostic tools are essential for improving patient outcomes. This study investigates the application of explainable Artificial Intelligence (XAI) techniques, specifically Grad-CAM (Gradient-weighted Class Activation Mapping), to enhance pneumonia prediction using chest X-ray images. We employ three well-known convolutional neural network (CNN) architectures—VGGNet16, ResNet50, and InceptionV3—on a diverse publicly available dataset consisting of over 5,000 chest X-ray images with balanced pneumonia-positive and pneumonia-negative cases. The dataset’s diversity in terms of image quality and patient demographics posed significant challenges, which were addressed through meticulous pre-processing and augmentation techniques. The methodology includes fine-tuning pre-trained models on this dataset, and employing standard data pre-processing steps such as normalization and augmentation to improve model robustness. The Grad-CAM technique is applied to visualize regions of interest within the X-ray images, providing interpretability by highlighting anatomical features associated with pneumonia. Evaluation metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), are used to assess model performance. The Grad-CAM visualizations not only illustrate how each model arrives at its predictions but also offer insights into potential clinical applications. ResNet50 achieves superior performance, particularly in accuracy and AUC-ROC, indicating its effectiveness in distinguishing between pneumonia-positive and pneumonia-negative cases. The study highlights the practical implications of integrating XAI techniques into clinical practice, enhancing the interpretability and trustworthiness of AI-based diagnostic tools. While the proposed models demonstrate promising results, the study acknowledges limitations such as dataset imbalance and generalizability. Future research will explore expanding the dataset, incorporating additional XAI techniques, and investigating integration into clinical workflows. In conclusion, this research demonstrates the potential of combining deep learning with XAI to advance pneumonia prediction, offering a step towards more interpretable and reliable medical imaging diagnostics.