Emotion Artificial Intelligence (AI) is rapidly redefining the landscape of human-computer interaction by enabling machines to interpret and respond to human emotions with nuanced understanding. This review explores the evolution and efficacy of multimodal emotion recognition systems that draw on diverse data streams, including social media text, vocal tone analysis, and facial expression tracking. While traditional unimodal approaches often fail to resolve contextual ambiguities in emotion detection, multimodal frameworks enhance reliability through the cross-validation of emotional cues. The integration of such heterogeneous data, however, presents substantial challenges—ranging from synchronization and scalability to interpretability and ethical data handling. This paper synthesizes state-of-the-art methodologies, highlighting recent advances in feature fusion techniques, deep learning architectures, and sentiment-aware signal processing. Applications across healthcare diagnostics, affective education systems, and emotion-driven marketing strategies are examined to illustrate real-world relevance. Furthermore, the paper addresses critical concerns surrounding data privacy, algorithmic bias, and the need for explainable and ethically governed AI models. By consolidating current trends and identifying key research gaps, this work aims to provide a foundational perspective for scholars and practitioners striving to build robust, transparent, and socially responsible emotion AI systems empowered by multimodal big data.

Keywords: Emotion Artificial Intelligence, Multimodal Data, Emotion Recognition, Deep Learning, Affective Computing, Ethical AI