Emotion-aware educational systems have gained significant importance in intelligent learning environments because student emotions directly affect learning performance, concentration, and engagement. This paper proposes an intelligent classroom emotion analysis framework using deep learning architectures for automatic facial emotion recognition. The proposed system utilizes Convolutional Neural Networks (CNN) and ResNet18 models for multi-class emotion classification in smart classroom environments. Unlike traditional FER systems, this work focuses on adaptive learning applications and intelligent educational analysis. Experimental evaluation is performed using accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. Results demonstrate that the proposed residual learning-based framework achieves 90% accuracy and significantly improves classroom emotion prediction capability.