This study presents a combined bibliometric analysis and systematic literature review of artificial intelligence (AI)_model-driven development (MDD) approaches for generating adaptive and intelligent user interfaces. Based on an initial set of 3,335 records retrieved from Scopus, a structured filtering process following preferred reporting items for systematic reviews and meta-analyses guidelines resulted in 51 relevant studies for in-depth analysis. The findings indicate a rapid growth in research within this area, particularly over the last five years, with substantial contributions from the fields of computer science and engineering. The analysis shows that current approaches increasingly rely on integrating machine learning techniques within model-driven pipelines to support context-aware adaptation, particularly in emerging environments such as augmented reality, virtual reality, virtual humans, and metaverse environments. This review further identified a set of dominant methodological trends, including AI-assisted code generation, models@runtime adaptation, and generative user interface approaches. However, several limitations remain evident in the literature, particularly regarding real-time adaptation, cross-platform interoperability, and energy efficiency. Overall, the results suggest that while AI_MDD combination has progressed toward more adaptive and intelligent interface generation, existing approaches remain largely experimental and lack standardized frameworks for deployment. These findings highlight the need for robust methodologies that combine model-based abstraction with data-driven adaptation in a consistent and scalable manner.

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