This talk reviews the rapidly expanding field of AI-driven emotion-aware platforms, with a focus on systems that infer affective states from speech, text, facial expression, and physiological signals. It examines some technical foundations of these platforms, including feature extraction, large language and speech models, and personalization. The review also considers major application domains, such as mental health support, coaching, education, customer service, and remote patient monitoring. Particular attention is given to the shift from static emotion classification toward interactive, adaptive systems that respond to users over time. The talk concludes by identifying priorities for rigorous evaluation, ethical deployment, and meaningful human-centered design.