This project, EmoBrace, addresses the challenges of subjective emotion evaluation by leveraging advancements in wearable technology and machine learning. This initiative aimed to develop a wearable bracelet equipped with sensors for real-time collection of physiological data, such as heart rate, blood pressure, and skin temperature. A Flutter-based mobile application with Firebase integration was created to visualize and store this data, providing a seamless user interface for account management, notifications, and bracelet monitoring. Machine learning models, including SVM, Random Forest, KNN, Logistic Regression, and XGBoost, were employed to analyze the physiological signals and classify emotional states in real-time. The final system delivers accurate, objective emotion detection, enabling emotion visualization and personalized feedback. This innovative solution highlights the potential of wearable technology to enhance human-computer interaction and contribute to emotion-aware systems. © 2025 IEEE.