Floods are the most frequent and destructive disasters, causing widespread destruction, A significant loss of life, and substantial economic impacts globally. Climate change exacerbates urban flood risks by altering precipitation patterns, increasing the frequency of extreme weather events, and raising sea levels. These escalating risks necessitate innovative solutions for effective urban flood management. Digital Twin (DT) technology offers a promising solution by providing virtual models and data-driven simulations that enhance urban landscape management, enabling timely flood warnings, evacuation planning, and property protection. Integrating Generative Artificial Intelligence (GenAI) further elevates digital twins by enhancing predictive capabilities and creating more realistic and interactive scenarios. Utilizing generative algorithms, digital twins can generate synthetic data to simulate a wide range of potential outcomes, improving the accuracy of modeling complex systems and forecasting variations and challenges. Moreover, GenAI improves the precision and reliability of digital twins in representing real-world environments through high-fidelity simulations. This study synthesizes findings from the literature review to develop a conceptual framework for elucidating the synergies between generative AI and digital twins in the context of urban flood risk assessment. It begins with an introduction to the integration of generative AI and digital twins for simulating flood scenarios. The paper then delves into the fundamentals of generative AI, discussing its principles, applications, and successful implementations across various domains, particularly in urban flood risk assessment. Subsequent sections examine the evolution of digital twins and their critical role in assessing, predicting, and mitigating flood risks. The study further investigates the intersection of generative AI and digital twins, highlighting the enhanced simulation capabilities provided by GenAI. Concluding with an in-depth analysis of specific applications of GenAI-enhanced digital twins in flood risk assessment, the study anticipates future challenges and advancements, emphasizing emerging trends and potential development opportunities.