In this sample-structured document, neither the cross-linking The rapid growth of digitization has led to a skyrocketing increase in Data Center (DC) energy consumption. By 2030, all DCs worldwide are expected to consume 13% of global energy demand, potentially up to 21%. This could lead to significant operational costs, power security impacts in the energy ecosystem, and environmental threats. Hence, optimizing a DC, particularly the class of High-Performance Computing (HPC) clusters, is a significant concern. Integrating the IoT, sensors, and intelligent devices has significantly contributed to generating vast operational management data from various aspects of the DC industry. Indeed, effectively modeling and processing this data could improve energy efficiency, ensure reliability, reduce operating costs, and sustainably manage DC. However, prior heuristics, statistical, and engineering methods could not be effective for modeling and simulating this data. In this work, the authors provide a holistic approach based on Blockchain Digital Twin (DT), augmented by Artificial Intelligence (AI) processed data to represent the DC physical complex system in virtual and tokenized models. The objective is to obtain near-real-time prediction, optimization, monitoring, controlling and improved decision-making. In detail, the methodology, the architecture and steps taken to develop a scalable analytical or "Analytical Dashboard"in Blockchain are shown. The pioneering Blockchain DC Digital Twin framework and approach, experimented in a real infrastructural setting (ENEA HPC "CRESCO 6") learns from actual operational data and allows the management, monitoring, and control in real-time visualization of whole DC, thus allowing a coverage of the DC status and the optimization of its functionalities. Our aim is also to show the achieved benefits of the Blockchain Digital Twin DC challenge for various stakeholders. © 2025 Elsevier B.V., All rights reserved.