This study presents a conceptual framework for dynamically adapting intelligent user interfaces (IUIs) based on user behavior and emotional states. Motivated by the need for intuitive, personalized interactions in Smart City applications, the approach leverages interaction metadata such as mouse movements, typing patterns, and navigation behavior to classify users into behavioral groups. By integrating an emotion recognition component, the framework extends traditional adaptive designs, enabling real-time adaptations. A browser extension and scalable server infrastructure facilitate the collection, preprocessing, and feature extraction from user interaction data. Although the emotion recognition module is currently conceptual, it aims to classify emotional states using supervised machine learning methods, providing significant benefits such as improved usability, personalization, and user satisfaction. This contributes towards bridging behavioral analytics and emotionally intelligent design, offering advancements for adaptive systems across diverse applications.