With 15% of working-age adults facing mental disorders and an annual loss of US$ 1 trillion in the world due to impaired productivity from depression and anxiety, the necessity for real-time emotional and physiological monitoring is paramount [1]. As similar levels of stress and mental health disorders are found among engineering students, mental health management is imperative in engineering education [2]. However, the high costs associated with mental health management tools, the necessity for additional gadgets, and rare usage among students pose significant barriers to widespread adoption and utilization in engineering education [3], [4]. In this study, we examine the integration of Remote Photoplethysmography (rPPG), a wireless stress measurement technology for real-time physiological monitoring by detecting light intensity variations on the skin. By advanced rPPG signal processing, Heart Rate Variability (HRV) metrics like Standard Deviation of Normal-to-Normal Intervals(SDNN), Root Mean Square of Successive Differences(RMSSD), and the Low-Frequency / High-Frequency Ratio(LF/HF) are calculated to offer stress insights. Our results resulted in an accuracy of 92% as validated with the ground truth dataset. Moving forward, we aim to enhance performance and deploy an app for widespread, low-cost access to stress management and monitoring.