Artificial Intelligence and Digital Twins in Smart Real Estate Asset Management: A Framework for Sustainable Urban Development

Abstract

The rapid urbanization of global economies, increasing pressure on infrastructure systems, and growing sustainability concerns have accelerated the adoption of advanced digital technologies within the real estate sector. Among these technologies, Artificial Intelligence (AI) and Digital Twins have emerged as transformative tools capable of reshaping real estate asset management practices through enhanced operational efficiency, predictive decision-making, and sustainability optimization. AI enables intelligent analysis of large-scale datasets generated by buildings, occupants, and infrastructure systems, while Digital Twins create dynamic virtual representations of physical assets that facilitate real-time monitoring, simulation, and performance optimization. This study employed a systematic literature review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, synthesizing approximately 180 peer-reviewed studies published between 2015 and 2026 across eight major academic databases. The review examined the technological ecosystem supporting intelligent asset management, including the Internet of Things (IoT), Building Information Modeling (BIM), cloud and edge computing, and big-data analytics, and investigated how AI-driven Digital Twin systems facilitate predictive maintenance, occupancy analytics, energy optimization, risk assessment, and lifecycle asset management. The findings indicate that AI-enabled Digital Twins substantially improve decision-making capability, operational resilience, and sustainability performance across the real estate lifecycle, with reported reductions in maintenance cost and improvements in energy efficiency and decision-making efficiency consistently documented in the empirical literature. The study proposes an integrated conceptual framework combining AI, Digital Twins, IoT, and sustainability principles to support next-generation smart real estate ecosystems, and identifies implementation challenges, research gaps, and future opportunities associated with autonomous building management, generative and explainable AI, and sustainable smart-city development. The framework provides guidance for researchers, practitioners, and policymakers seeking to accelerate digital transformation within the built environment.