Stress, a prevalent issue in today's fast-paced world, has significant implications on physical and mental health. Recognizing the global impact of stress-related diseases, we embarked on a journey to develop an intelligent stress detection system. Our approach integrates facial expression analysis, landmark detection, and deep learning to provide real-time assessment of stress levels. Leveraging a dataset of facial images and deep learning models, we customized a Convolutional Neural Network (CNN) as an optimal choice for emotion recognition, achieving remarkable results. Incorporating physiological indicators, we devised a stress calculation formula that categorizes stress into four states. Our user-friendly web interface empowers users to monitor their stress trends overtime. This research showcases the effectiveness of learning based assistive technologies for stress management and significant improvement in accuracy of classifier models.