Pneumonia is a global health concern, especially in underserved regions. Traditional diagnostic methods, relying on costly chest X-rays, suffer from interpretation variances. To overcome these challenges, we’ve developed an advanced computer-assisted evaluation system utilising deep transfer learning techniques. Our approach aims to improve diagnostic accuracy and accessibility, particularly in resource-limited settings, offering a promising solution to enhance pneumonia diagnosis globally. Using a large dataset gathered from Kaggle, our novel approach uses convolutional neural network (CNN) models such as VGG16, ResNet-50, and InceptionNet-v5 to autonomously detect pneumonia in chest X-ray pictures. Deep transfer learning helps our models overcome data scarcity limits, allowing them to accurately recognise relevant visual attributes and patterns. Our methodology employs an ensemble approach, combining the strengths of each CNN model. We introduce a groundbreaking strategy for calculating optimal weights based on key evaluation metric such as accuracy. Evaluation using RSNA dataset shows remarkable accuracy: 92% with the CNN model and 84% with ResNet-50, promising improved diagnosis, especially in resource-constrained settings, potentially saving lives. This innovative approach represents a significant leap forward in pneumonia diagnosis, offering a scalable and reliable solution for healthcare providers globally. To sum up, our computer-aided diagnosis method offers a state-of-the-art approach to addressing the difficulties involved in diagnosing pneumonia. By combining ensemble modeling with deep transfer learning methods, we have created a very useful tool for correctly detecting pneumonia in chest X-ray pictures. This technology offers enormous promise for enhancing healthcare delivery and outcomes globally with additional development and use.