With the rapid advancement of digital infrastructure, computer and communication network management is no longer limited to monitoring devices and repairing failures after they occur. Modern research and technological developments are increasingly focused on creating intelligent systems capable of simulating real-world networks, analyzing their behavior, and evaluating potential changes before implementation. One of the most promising technologies in this field is the Digital Twin Network (DTN), which represents a significant advancement in network design, management, and performance optimization. This technology relies on creating a virtual model that replicates a physical network, including its components, connections, data traffic, and operational conditions. By continuously collecting information from network devices, the digital twin allows engineers to evaluate network performance, identify potential bottlenecks, and test new configurations in a virtual environment before applying them to the actual infrastructure. For example, when planning to expand a computer network within a university or a large organization, a digital twin can simulate an increase in the number of users, changes in data transmission paths, or the failure of a network router. These simulations help engineers compare alternative solutions, identify potential risks, and minimize unexpected service interruptions during network modifications. The importance of this technology becomes even greater when integrated with data analytics and machine learning algorithms. Operational data can be analyzed to detect abnormal patterns, predict potential performance degradation, support proactive maintenance, and optimize network resource allocation. However, the accuracy of these predictions depends on the quality of the collected data and how effectively the virtual model represents real-world network conditions. At the international level, Digital Twin Networks have attracted considerable attention from organizations responsible for developing communication standards. The International Telecommunication Union (ITU) has introduced technical recommendations addressing the fundamental requirements, architecture, management capabilities, and security considerations associated with these systems. Despite their promising capabilities, implementing Digital Twin Networks presents several engineering challenges. These include the complexity of developing accurate models for large-scale networks, the need for continuous data synchronization, computational resource requirements, and cybersecurity concerns related to protecting sensitive network information. Digital Twin Networks represent an important direction in the future of computer and communication engineering. They provide valuable opportunities for researchers and engineers to develop more efficient, resilient, and intelligent network management solutions. By enabling technical decisions to be evaluated virtually before implementation, this technology can contribute to building more reliable digital infrastructures capable of meeting future technological demands. References 1. International Telecommunication Union (ITU-T). Recommendation Y.3090: Digital Twin Network – Requirements and Architecture. 2022. 2. International Telecommunication Union (ITU-T). Recommendation Y.3092: Digital Twin for Management and Orchestration in IMT-2020 Networks and Beyond. 2024. 3. International Telecommunication Union (ITU-T). Recommendation X.2011: Security Guidelines for Digital Twin Networks. 2024. Al-Mustaqbal University The First University in Iraq