Facial Emotion Recognition (FER) is a key technology for identifying emotions based on facial expressions, with applications in human-computer interaction, mental health monitoring, and customer analysis. This study presents the development of a real-time emotion recognition system using Convolutional Neural Networks (CNNs) and OpenCV, addressing challenges such as varying lighting and facial occlusions. The system, trained on the FER2013 dataset, achieved 85% accuracy in emotion classification, demonstrating high performance in detecting happiness, sadness, and surprise. The results highlight the system's effectiveness in real-time applications, offering potential for use in mental health and customer behavior analysis.