Stress detection from facial emotions is a promising area for IT employees, as stress can have a significant impact on their well-being, job satisfaction, and productivity. The ability to detect stress from facial expressions could help IT companies identify when employees are experiencing stress and provide support and resources to help them manage their stress levels. There are several approaches to stress detection from facial emotions, including machine learning algorithms that analyze facial expressions in real-time. These algorithms can be trained on a dataset of facial expressions and associated stress levels, allowing them to identify patterns in facial expressions that are indicative of stress. One challenge with stress detection from facial expressions is that facial expressions can be ambiguous and subjective, and individuals may express stress in different ways. Therefore, it is important to use a large and diverse dataset for training the algorithm, and to validate the algorithm's performance on a variety of individuals and situations. To implement stress detection from facial emotions for IT employees, companies could integrate this technology into their existing monitoring systems. In this project implement system a camera could be installed in the workplace to capture employees' facial expressions, and an algorithm could analyze the video feed in real-time to detect signs of stress. This information could be used to provide personalized support and resources to employees. Overall, stress detection from facial emotions has the potential to be a valuable tool for IT companies to improve the well-being and productivity of their employees. Facial emotions are classified using Convolutional neural network algorithm to improve the accuracy in stress detection in real time environments. Keywords- CNN – Convolutional Neural Network, ML- Machine Learning, Deep Learning, Artificial Intelligence.