Pneumonia is a dangerous lung infection that continues to be one of the world’s top causes of sickness and death. It is particularly dangerous in areas with poor healthcare systems. Chest X-ray imaging is the main clinical tool used to detect pneumonia, and prompt and accurate diagnosis is essential for successful therapy. However, the subjective and time-consuming nature of image interpretation necessitates the use of automated diagnostic methods. Convolutional neural networks (CNNs), one type of deep learning technique, have shown a lot of promise in increasing diagnostic precision and lowering the workload for medical practitioners. Four CNN architectures were used in this study to assess how well they detected pneumonia from chest X-ray images: DenseNet121, ResNet50, InceptionV3, and EfficientNetB0. According to the testing results, DenseNet121 surpassed all of the others with an astounding accuracy of 96.15%, ResNet50 reached 94.07%, InceptionV3 reached 95.19%, and EfficientNetB0 reached 91.51%. The most dependable architecture among these models was DenseNet121 because of its excellent performance, effective feature extraction, and computational viability. Therefore, it is advised that DenseNet121 be incorporated into clinical decision-support systems to help with the quick and precise detection of pneumonia, particularly in distant or underfunded healthcare settings where skilled radiologists might not be easily accessible.