Chest X-rays are one of the most widely used diagnostic tools for identifying lung-related diseases such as pneumonia, tuberculosis, and lung cancer. However, manual interpretation of these images can be time-consuming and subject to inconsistencies, especially in high-pressure clinical settings or areas with limited access to radiologists. To address these challenges, this study introduces a deep learning-based system that leverages Convolutional Neural Networks (CNNs) for automatic disease detection from chest X-ray images. The model is trained on a large, diverse dataset and incorporates essential preprocessing steps like histogram equalization to improve image quality and enhance diagnostic accuracy.The CNN-based framework is designed to automatically extract features and classify X-ray images into multiple disease categories. It aims to reduce diagnostic delays and support radiologists by offering fast and consistent evaluations. The system’s performance is assessed using standard metrics such as accuracy, sensitivity, specificity, and F1-score, demonstrating its effectiveness as a decision-support tool in clinical workflows. By reducing dependence on manual review, this approach enhances both scalability and reliability in medical imaging diagnostics.Beyond automated image analysis, this paper also explores the integration of a real-time health monitoring solution for elderly individuals using a smartwatch-based system. This wearable device tracks vital signs such as heart rate and physical activity and provides instant alerts to caregivers in the event of abnormal readings. The combined application of AI-powered diagnostics and wearable health tracking presents a dual solution that supports early disease detection and improves continuous patient monitoring – paving the way for more responsive, accessible, and patient-centered healthcare.