Emotion AI and Machine Learning, commonly referred to as Affective computing, is an emerging branch of artificial intelligence that enables machines to understand, interpret, and respond to human emotions in a meaningful way. By integrating principles from computer science, psychology, and data analytics, this field aims to create more natural and empathetic interactions between humans and computer systems. One of the primary approaches in emotion recognition involves analyzing facial expressions captured through a camera to identify emotional patterns such as happiness, sadness, anger, surprise, and stress. The proposed application focuses on real-time facial emotion detection using machine learning algorithms that extract facial features, classify emotional states, and display the calculated probability percentages of each detected emotion. By presenting emotion scores in percentage form, the system provides a clear and quantitative understanding of the user’s emotional state. Such technology has significant practical applications in areas including human-computer interaction, customer behavior analysis, online learning engagement, healthcare monitoring, and mental health assessment, thereby contributing to the development of intelligent, responsive, and emotionally aware systems.