The integration of Emotional Artificial Intelligence (Emotional AI) into customer engagement strategies is reshaping how organizations interpret and respond to human affective states. This paper presents a structured review of frameworks supporting Emotional AI deployment within customer interaction channels, emphasizing real-time sentiment recognition, adaptive feedback mechanisms, and personalized emotional responses. It explores how emotion-aware technologies ranging from facial expression analysis to voice modulation and biometric sensing are embedded in customer service workflows to enhance satisfaction and loyalty. The study critically examines the architectural models that underpin these systems, including hybrid AI-human decision-making loops and data governance protocols essential for ethical use. Key challenges such as bias in emotion recognition, data privacy concerns, and cultural variability in emotional expression are also discussed. By mapping current deployments and theoretical models, the paper offers a conceptual foundation for future research and practical implementation of Emotional AI in dynamic customer-facing environments.