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In recent years, the application of artificial intelligence (AI) in immovable cultural heritage research has expanded rapidly, supporting new approaches to heritage documentation, condition assessment, conservation, management, and public communication. In this study, immovable cultural heritage primarily refers to historic buildings, archaeological sites, monuments, cultural landscapes, and other heritage places with fixed spatial locations. Movable cultural objects, museum collections, and intangible cultural heritage are not systematically examined unless the relevant studies directly address the documentation, monitoring, restoration, conservation, or management of immovable heritage. Despite the growing volume of research, existing studies have largely focused on individual technical applications, while comprehensive bibliometric analyses and knowledge-structure mapping from a global perspective remain limited. To address this gap, this study retrieved 382 English-language publications from the Web of Science Core Collection covering the period from 1 January 2015 to 1 October 2025. Using bibliometric methods and analytical tools including VOSviewer, CiteSpace, and ArcGIS, the study examines publication trends, geographical distribution, disciplinary categories, major authors, institutional affiliations, collaboration networks, thematic evolution, and emerging research topics. The results show that publication output increased substantially after 2020. China, Italy, and Spain were among the most productive countries and occupied important positions in the international collaboration network. Major research themes included digital heritage, structural health monitoring, virtual reconstruction, deep learning, remote sensing, and sustainability-related applications. These findings indicate that AI-related research on immovable cultural heritage is strongly represented in engineering, conservation science, computer science, remote sensing, and related technical fields. However, this pattern should not be interpreted as evidence of a general disciplinary shift across cultural heritage studies as a whole. Rather, it reflects the technological orientation of the literature captured by the present search strategy, which was limited to English-language publications indexed in the Web of Science Core Collection. This study maps the development and knowledge structure of AI applications in immovable cultural heritage research and provides quantitative evidence for identifying major research areas, collaboration patterns, methodological tendencies, and future research needs.