This paper explores the effectiveness of a hybrid CNN-Transformer model for pneumonia identification from chest X-ray pictures is investigated in this work. We obtain higher accuracy and enhanced localisation of regions afflicted by pneu- monia by utilising Transformers’ global context awareness and CNNs’ spatial feature extraction capabilities. Furthermore, the hybrid loss function guarantees improved infection zone segmentation by fusing IoU and binary cross-entropy. Clinical investigations and current research show that our method works far better than traditional CNN models, leading to considerable gains in diagnosis accuracy. The results validate possible uses in real-time medical imaging systems as proposed in recent research.