As telecommunications infrastructure evolves toward Beyond 5G (B5G) and 6G paradigms, artificial intelligence (AI) and machine learning (ML) have emerged as indispensable tools for driving network optimization, automation, and intelligent orchestration. However, a critical gap exists between developing AI/ML models and deploying them confidently in production environments. Traditional evaluation mechanisms, relying on static digital twins, isolated testbeds, or offline simulation, struggle to scale or generalize across diverse deployment scenarios. These conventional methods lack the capability to simulate nuanced, real-time conditions, often leading to brittle model performance when confronted with unforeseen network behaviors. This creates operational risks, delays in AI adoption, and unnecessary overhead for network operators. To address these limitations, we introduce GENATWIN (Generative AI-based Network Digital Twin), a novel framework that harnesses the power of conditional generative adversarial networks (cGANs) to synthesize realistic network behavior under dynamically configurable scenarios. GENATWIN is built upon the concept of DTAC (Digital Twin Augmenting Condition), a flexible encoding mechanism that allows operators to simulate contextual features such as traffic patterns, mobility profiles, and network topologies. By training generative models on historical and real-time data, GENATWIN produces synthetic but highly realistic KPI traces for evaluating AI model behavior under diverse operational conditions. The framework supports iterative model validation, automated stress testing, and scenario-specific AI performance analysis, empowering telecom operators to achieve faster, safer, and more confident deployment of AI features. This chapter provides a comprehensive overview of the GENATWIN framework, detailing its layered architecture, the design and training of its cGAN components, simulation workflows, and scalability. We further demonstrate its real-world applicability and potential to significantly reduce operational expenditure (OPEX) while enhancing the robustness of AI-powered network functions. Finally, we discuss future directions, including diffusion-based modeling and integration with real-time closed-loop control systems, positioning GENATWIN as a foundational enabler for AI-native networks in the 6G era.