Pneumonia continues to be a worldwide health concern, necessitating an accurate and timely diagnosis to improve treatment outcomes and lower mortality rates. This paper explores the transformative potential of Convolutional Neural Networks (CNNs) in the Chest X-Ray (CXR) image-based pneumonia detection, focusing on EfficientNet in particular. Examining the incidence and potentially fatal consequences of pneumonia, the study highlights the vital need for precise diagnostic instruments. Current approaches are analyzed critically, highlighting CNNs’ indispensable role in identifying complex patterns in CXR images. The EfficientNet model is trained using a carefully selected and varied dataset in order to teach it to identify minute visual cues that may indicate pneumonia. Thorough validation processes show a remarkable 93% accuracy in the diagnosis of pneumonia. These results highlight the potential of CNN-based models, especially EfficientNet, to significantly improve detection accuracy. They also highlight the importance of AI-driven methods for better clinical judgment in the management of pneumonia and, eventually, better patient outcomes.