Building Information Modeling-based (BIM) Digital Twins (DT) are increasingly adopted to support sustainable building management; however, the growing body of secondary literature—systematic reviews, surveys, and bibliometric studies—remains methodologically fragmented and lacks a consolidated tertiary synthesis. This fragmentation prevents researchers and practitioners from identifying consistent guidelines, validated frameworks, and comparable software ecosystems across the field. To address this gap, this study conducts a tertiary review of 57 secondary studies published between 2018 and 2025, following Kitchenham and Charters’ evidence-based guidelines and the PICOC framework. Data extraction and comparative analysis were conducted across seven criteria, including methodological approaches, proposed frameworks, enabling technologies, software tools, and reported limitations. The results reveal that bibliometric analyses and systematic literature reviews dominate the field, with few yielding structured frameworks and taxonomies; nearly half of the reviewed studies do not assess specific software platforms. Artificial intelligence and the internet of things are among the most widely studied enabling technologies, primarily associated with energy management, predictive maintenance, and structural monitoring, yet their integration with BIM-based DT platforms is inconsistently documented. The findings expose persistent interoperability constraints, insufficient data governance structures, limited maturity models, and a lack of large-scale empirical validation—gaps that this tertiary synthesis maps and prioritizes to guide future research toward reproducible, scalable, and sustainability-oriented DT implementations in the architecture, engineering, and construction sector.