The prevalence of stress and anxiety among undergraduate students is a growing concern worldwide, impacting academic performance, mental health, and overall well-being. Currently, an alarming number of adolescents and young adults are experiencing anxiety and depression. Mental tension is one of the primary causes. This research paper aims to investigate the psychometric educational stress and anxiety among undergraduate students, especially in engineering colleges using machine learning algorithms. Various psychometric scales like Perceived Stress Scale (PSS), General Anxiety Disorder 7-item (GAD-7) scale, Patient-Reported Outcomes Measurement Information System (PROMIS) three outcome variables like stress, anxiety, and depression are analyzed and few machine learning techniques, like Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking provides insights into the factors contributing to stress, anxiety and depression levels among undergraduates. Also, two machine learning models logistic and Support Vector Machine (SVM) are proposed to predict the stress level of the students.