Stress is a major health concern that significantly affects mental stability and can have adverse effects on physical well-being if prolonged. Early detection of stress can help and prevent health-related issues. Individual stress patterns are detected using a variety of bio-signals, including thermal, electrical, auditory, and visual cues which are invasive methods. But according to the well-known saying statement, “Face is a mirror of mind,” one can observe one’s emotion or mental state on one’s face. Based on this, Investigated the potential of using facial expressions as a non-invasive method to detect stress levels. Facial expressions could be analyzed and classified as stress and non-stress by examining facial expressions. To solve this problem we have used pre-trained network models—Inception, Xception, MobileNetv2, Vgg19, EfficientNet deep learning models, and AffectNet Dataset for stress detection and also represent the comparative study of networks based on confusion and performance metrics. Testing on a separate set of data of images indicates that the MobileNetv2 and Xception models give more accuracy for stress detection.