O Pneumonia is a significant worldwide health issue, with numerous etiologies, such as bacteria, fungi, and viruses, contributing to its challenging diagnosis. Computed tomography (CT) imaging is crucial for the diagnosis of pneumonia because it provides detailed information on lung abnormalities. During the pandemic, many hospitals have adopted CT scans to identify lung diseases caused by respiratory infections. Not only is it more expensive, but it’s not as widely accessible. Early, precise, and cost-effective identification of pneumonia problems is necessary for improved treatment outcomes. Pneumonia-Plus, which uses convolutional neural network (CNN) architecture, was trained on a large set of annotated CT scans that included cases of bacterial, fungal, and viral pneumonia. We have optimized the system to independently recognize distinct features associated with multiple pneumonia causes. This work utilized a deep learning model, dubbed Pneumonia-Plus, to categorize viral, bacterial, and fungal pneumonia using chest photos. The experiment shows that Pneumonia-Plus performs better overall by classifying pneumonia cases with accuracy. Most importantly, the model achieves the best accuracy and recall rates while showing resilience in distinguishing between bacterial, viral, and fungal pneumonia.