Human lung disorders, especially pneumonia, remain a significant worldwide health issue, leading to millions of hospitalizations and deaths annually. Early detection and accurate diagnosis are crucial to improving patient outcomes; however, the manual process of interpreting chest X-rays can be time-consuming and prone to human error. Artificial intelligence (AI) has demonstrated encouraging promise in recent years for automating and enhancing medical imaging diagnostic accuracy. In this paper, the deep learning model is developed using MobileNetV2, a thin convolutional neural network architecture, to divide chest X-ray pictures into two groups: healthy lungs and pneumonia. AI-Driven Chest X-Ray Analysis Using MobileNetV2 for Pneumonia Diagnosis. The project’s dataset was sourcedfrom Kaggle, containing labeled X-ray images of normal lungs and lungs affected by pneumonia. The MobileNetV2 architecture was selected due to its harmony between computational efficiency and classification performance, making it particularly suitable for deployment on mobile devices and edge computing platforms. Google Colab was used to train and optimize the model, utilzing its powerful GPU resources for faster computation. The propsoed approach aims to provide an accessible and scalable result for lung disease detection, potentially aiding in remote diagnostics and reducing the problem on healthcare systems in resource-constrained settings. Accuracy, precision, recall, and F1 score were among the performance metrics evaluated on a different test set, and the final model classified pneumonia and normal cases with high accuracy. This project demonstrates that AI-powered diagnostic tools which can offer a viable solution to augment clinical decision-making, improve diagnostic speed and accuracy, and ultimately contribute to better health outcomes in the fight against lung diseases like pneumonia.