Emotions are pivotal in influencing students’ learning experience, academic achievement, and general well-being. Fear and anxiety, especially, are hindrances to cognitive processes like memory, concentration, and problem-solving, usually evoked by high-stakes academic contexts. Conventional self-reported questionnaires and psychological tests employed for fear measurement are plagued by subjectivity, response bias, and a lack of ability to reflect real-time emotional fluctuations. To overcome these constraints, this research presents PsyVisionNet, a multi-modal deep learning platform that incorporates computer vision-based facial emotion analysis and psychological parameters (heart rate, stress level, and anxiety scores) for real-time fear recognition among students. The proposed model utilizes a pre-trained ResNet-18 to extract facial features associated with fear and a Multi-Layer Perceptron (MLP) to process physiological information. These characteristics are integrated in a decision-making layer for categorizing students into “Phobic” or “Non Phobic” classes. PsyVisionNet yields 92.5% accuracy with an ROC of 0.91, proving the suitability of this method for objective assessment of fear. In contrast to traditional techniques, this system enables continuous, scalable, and real-time monitoring of emotions, thus allowing interventions on time in education. Potential future uses of PsyVisionNet are in personalized learning, emotional support for mental health, and incorporation into Virtual Reality (VR) environments to provide immersive emotion-aware learning and therapy. This research fills the gap between AI-based emotion detection and psychological measurement, providing a new, data-driven method of monitoring student emotions in actual learning settings.