Intelligent User Interfaces (IUIs) merge artificial intelligence and human–computer interaction to deliver context-aware, adaptive experiences. Yet research still lacks a cohesive view of how IUIs are integrated into software design/engineering and how they affect software quality. This systematic mapping study reviews 366 peer-reviewed works to identify gaps in IUI implementation across the development lifecycle. We analyze five dimensions: deployment environments, application domains, development phases, implementation techniques, and quality characteristics. IUIs are chiefly realized in web and mobile applications using machine learning, user modeling, and natural language processing. While gains in interaction and functional suitability are reported, security, reliability, and early-phase user involvement remain underexplored. Our findings call for stronger evaluation methods, standardized definitions, and tighter integration of AI-based interfaces into formal engineering workflows. The study offers a data-driven overview that bridges AI and software engineering, laying a foundation for quality-driven, automated adoption of IUIs.