University students increasingly face mental health challenges, including anxiety, depression, and stress, yet most higher education environments lack proactive systems for emotional monitoring and support. This study aimed to design, implement, and evaluate a hybrid teaching management system that integrates narrative simulation with visual artificial intelligence (AI) to promote mental health literacy and emotional resilience among students. The system includes a narrative decision-making module simulating stress scenarios and an AI-powered emotion recognition tool (based on facial expression detection) embedded in classroom settings. A mixed-methods design was employed with 332 undergraduate students and 15 faculty members from multiple universities. Pre- and post-intervention surveys, usage logs, and real-time emotional data from visual AI were collected. Quantitative data were analyzed using descriptive statistics, paired t-tests, ANOVA, and multiple linear regression. Results indicated statistically significant improvements in students' mental health scores (p < 0.01), emotional awareness, and decision-making confidence. This integrated approach demonstrates both usability and scalability, offering instructors early emotional insight and students a reflective learning environment. The method is best suited for institutions equipped with AI-capable classrooms and trained ethical oversight.