Accurate pneumonia diagnosis is crucial for reducing mortality rates, particularly in resource-constrained healthcare facilities. A deep learning detection framework that examines pneumonia diagnosis for chest X-ray images constitutes the proposal of this research. This system leverages the EfficientNetB7 architecture with Squeeze-and-Excitation (SE) blocks, which significantly increases feature extraction and outperforms the baseline EfficientNetB0 in distinguishing pneumonia from normal cases. The data undergoes systematic division into three parts for training, validation, and testing purposes as part of a thorough model evaluation. The final model achieves an impressive detection accuracy of 98.31%, surpassing existing approaches in this domain. To enhance interpretability, Grad-CAM heat maps are employed to highlight the most influential regions in the X-ray images, aligning with clinical diagnostic needs. This visualization-driven approach improves trust and transparency in AI-assisted medical decision-making, making it a valuable tool for pneumonia diagnosis.