The Architecture, Engineering and Construction (AEC) sector is central to the global economy, contributing significantly to GDP and providing employment to millions of people. However, it faces challenges such as low productivity, high construction costs, project delays and poor sustainability. In fact, the sector is responsible for a significant share of CO₂ emissions and consumption of natural resources, making it urgent to transform the sector towards more sustainable practices. In this context, the digital transition is proving to be a crucial element, driven by the adoption of advanced technologies such as Building Information Modeling (BIM), Digital Twin (DT), Internet of Things (IoT) and artificial intelligence (AI). These technologies, along with targeted investments and stricter regulations, intend to optimize the life cycle of buildings and reduce operating costs. In this context, this study aims to formulate a methodological proposal that combines these technologies to improve building management and maintenance practices. Applied to individual buildings, thanks to an automated comparative evaluation of digital models and the analysis of static and dynamic data of similar buildings, the methodology could optimize maintenance, with benefits in terms of durability, sustainability and cost reduction. At the urban scale, this methodology could improve the management of building stock, contributing to make cities safer and more sustainable.