Due to the adverse effects of stress on students’ health and academic success, it is to create an effective method for stress detection. This study is used to predict student stress by combining biosignal data with image processing techniques. Employs various image pre processing of facial images in this research like Gaussian Blur, Median Blur, Bilateral Filters, and Canny edge detection to enhance the extraction of features. The Multi-task Cascaded Convolutional Neural Network (MTCNN) detects and aligns facia landmarks for precise data collection. Wearable device data on respiration, oxygen, temperature, and heart rate is combined with previously processed images. The robust dataset from physiological and visual characteristics may benefit future stress prediction machine learning applications. This comprehensive real-time stress detection method allows the development of customized interventions to improve student health and academic performance. The author is developing new artificial minority oversampling methods and improving machine learning models. The list includes KNN, Decision Trees, Random Forests, and XG Boost. The author has achieved accuracy rates and F1-macro score over 90% with KNN and SVM and expects even better results with other supervised machine learning algorithms.