Urban infrastructure assets transition through various operational states throughout their lifecycle, from installation to decommissioning. Tracking and storing the spatiotemporal evolution of these states is crucial for ensuring accurate and reliable asset information, which can further support advanced analysis. However, managing the complexity of both spatial and temporal data, while maintaining the interconnectedness of these data, poses challenges that require flexible and adaptive database solutions. In addressing this matter, this research explores the use of graph databases in managing spatiotemporal evolution in asset maintenance by modelling the asset lifecycle events of indoor building assets using the Neo4j graph database that utilises labelled property graph (LPG) data structure. The research also conceptually explores the integration of the approach with blockchain technology to enhance the data integrity within the graph database. The study models lifecycle events using a point-based and entity-centric approach to represent temporal dimensions. The findings demonstrated how extracting the asset lifecycle events path and the chronology of the assets’ operational state and maintenance action showcase the flexibility of the graph database in managing the data and the conceptual framework for blockchain-enhanced security and verification. These results establish a foundational approach for applications in smart cities and sustainable development, including predictive maintenance and infrastructure resilience analysis.