Machine Learning in Architecture: A Bibliometric Analysis Approach of Trends, Influential Authorship, and Research Gaps from 2020 to 2024

This paper presents the findings of the bibliometric analysis investigating how the current research landscape of machine learning in the discipline of architecture has evolved between 2020 and 2024. This study employs a bibliometric methodology, drawing insights from peer-reviewed journal articles from 2020 to 2024, which were sourced from databases such as Web of Science, Scopus and Science Direct. Keywords were refined using Boolean operators to identify dominant trends, key application areas and ongoing research gaps. The goal of this paper is to provide a structured knowledge base that can guide future research by highlighting emerging directions. This study serves as the foundational phase of a broader doctoral investigation into the integration of machine learning in architecture. It contributes valuable data on keyword frequency, citation impact, key journals, leading authors, and institutional contributions. The findings provide a broad overview of the current outlook of machine learning in architecture, highlighting how machine learning as a transformative technological advancement is reshaping architectural research and design, as well as interdisciplinary collaboration, offering recommendations for future research priorities in the field of architecture.