Attention-deficit/hyperactivity disorder (ADHD) presents unique challenges in maintaining focus, managing distractions, and sustaining productivity. This study proposes a multimodal AI-powered interface that leverages facial expression analysis, sentiment recognition, and adaptive soundscapes to provide personalized focus reminders. By detecting user engagement levels through computer vision and emotion analysis, the system dynamically adjusts audio tracks, white noise, or motivational cues to help users regain focus. Additionally, it offers subtle, real-time nudges based on detected mood fluctuations, promoting self-awareness and cognitive regulation. This approach integrates machine learning, human-computer interaction (HCI), and affective computing to create a supportive, responsive, and personalized productivity assistant for individuals with ADHD. The findings will contribute to the development of emotion-aware assistive technologies that enhance focus and well-being.