Pneumonia has killed many children worldwide in recent decades. Pneumonia is one of the leading causes of death for children. It can be brought on by fungi, viruses, or bacteria. Early treatment of pneumonia can reduce the mortality rate in children. The best technique for predicting paediatric pneumonia is to use deep learning in conjunction with stacking classifiers. Every year, pneumonia kills almost 800,000 children, according to the United Nations Children’s Fund. There are two phases to the implementation of the stacking classifier. Random Forest, K-Nearest Neighbours, Logistic Regression, XGBoost, and Support Vector Classifier (SVC) are among the classifiers used in the first stage. The second stage involves Logistic Regression, which consolidates the predictions from the first stage to produce the final classification. Traditional model architectures often rely on convolutional neural networks (CNNs) and require large datasets for training. In this research, DenseNet and ResNet models were employed alongside a stacked ensemble classifier. This ensemble integrates methods such as Support Vector Classifier, AdaBoost, and Bagging Classifier to enhance predictive performance. A dynamic user interface was also developed to process and visualize images in real-time, eliminating the need for step-by-step handling of large datasets. When compared to previous models, this method shows better performance metrics. This project’s main objective is to put this system in an actual real-world production environment so that it can help diagnose paediatric patients with pneumonia, allowing for faster identification and more efficient treatment.