Background and Objective: The residential sector is one of the key areas for intervention to achieve urban sustainability due to its continuous energy consumption, extensive reliance on heating and cooling systems, and substantial contribution to carbon emissions. However, reducing energy consumption in residential complexes often conflicts with objectives such as ensuring residents’ thermal comfort, lowering costs, managing energy loads, and reducing carbon emissions. Therefore, conventional design and decision-making approaches based on trial and error are inadequate for addressing the complexities of sustainable housing design, as they cannot simultaneously account for multiple variables and the nonlinear relationships among them.
Methodology: This study aims to map the knowledge structure, conceptual evolution, thematic clusters, and research gaps in this field. A bibliometric and scientometric approach was adopted. Data were retrieved from the Web of Science (WoS) and Lens databases. Following the screening and removal of duplicate and irrelevant documents, 167 English-language research and review articles published between 2021 and 2026 were included in the analysis. Data analysis was conducted using VOSviewer and Bibliometrix and involved examining co-authorship networks, keyword co-occurrence, temporal trends in publication output and citations, source analysis, Bradford’s law, Lotka’s law, multiple correspondence analysis, and other bibliometric techniques.
Results and Findings: The findings indicate that energy efficiency and energy consumption remain at the conceptual core of the literature; however, research has rapidly moved toward integrating artificial intelligence, machine learning, evolutionary algorithms, energy management, renewable energy, and multi-objective optimization. As the most prominent emerging topic, multi-objective optimization provides a framework for balancing energy consumption, carbon emissions, thermal comfort, and grid performance. Nevertheless, limited field validation, insufficient attention to actual occupant behavior and building life-cycle carbon emissions, fragmented scientific collaboration networks, and limited model interpretability constitute the major research gaps in this field. Therefore, the future development of this research domain lies in the development of transparent, data-driven, and context-sensitive decision-support platforms capable of simultaneously optimizing environmental objectives and residential quality in the design of residential complexes.