The rapid growth of artificial intelligence in education has enabled new opportunities for enhancing student engagement and personalized learning. Traditional e-learning systems and classroom instruction often overlook the affective state of students, which plays a critical role in learning effectiveness. This research proposes an emotion-aware adaptive learning system that leverages facial emotion detection to suggest or predict the most suitable learning model for each student. Using real-time image capture, preprocessing, and deep learning-based emotion recognition, the system identifies students' emotional states such as happiness, confusion, boredom, or frustration. These insights are integrated with the learner's profile and past performance to recommend adaptive strategies, such as adjusting content complexity, pacing, or teaching methodology. The approach aims to improve student engagement, knowledge retention, and overall academic performance by bridging the gap between affective computing and personalized learning environments.