AI-native, programmable, and disaggregated 6G networks will be highly dynamic and distributed, demanding tools that can explain, predict, and safely optimize behavior across the edge–cloud continuum. Network Digital Twins (NDTs) promise this capability, yet current efforts in research and industry are fragmented and lack widely accepted formal definitions and architectural guidelines. This paper proposes a structured framework for NDTs in 6G, addressing these gaps by refining the conceptual foundations of NDTs, introducing a functional architecture, inherited from the 6G-TWIN EU consortium, and clarifying key components such as AI-driven workflows, the place of simulation, data management, and orchestration. Concrete examples illustrate how these components enable network automation, optimization, and predictive analytics. The paper proceeds by reviewing related work and standardization efforts, specifying functional and non-functional requirements, presenting the architecture and its various domains, and detailing lifecycle management across cloud to edge. We then report early implementations and evaluation results, and discuss security, privacy, and governance considerations, concluding with directions for validation and uptake. The key objective is to offer a cohesive reference model that guides the community in shaping NDT development, ensuring interoperability, scalability, adaptability, and seamless integration into AI-native 6G networks for improved intelligence and efficiency.