Providing structured data and maintaining the Asset Information Model (AIM) in a Digital Twin (DT) environment has been a source of constant debate, and the data provenance system utilizing a dynamic and real-time updated AIM on a blockchain platform for Predictive Asset Management (PAM) establishes an essential feature for specialists in this field. The purpose of this study is to develop a data Provenance Model on Blockchain-based DT focusing on PAM in Building facilities. Qualitative research data have been obtained from a wide range of secondary sources of data as scoping review. Afterwards, interviews have been conducted of industry specialists as primary source of data. Following the completion of the analyses, the resulting information has facilitated the development of Blockchain-based DT data provenance models and scenarios. Assessment of the proposed Data Provenance Model on Blockchain-based DT has been based on scenario as part of the case study involving a conference room in the Office Building at Stockholm with implementation of the Remix Ethereum platform and Sepolia testnet. The proposed Data Provenance Model ensures that data used in PAM processes is reliable and trustworthy by providing a transparent and immutable record of data origin, ownership, and lineage. The Data Provenance Model will enable decentralized applications to publish real-time data such as condition monitoring, assessment, prediction, and maintenance planning obtained from dynamic operations and maintenance data, thereby enhancing data reliability and effectiveness.