Abstract: The exploration of flow-induced vibration in fuel rods is of utmost importance as it plays a pivotal role in comprehending and mitigating factors that contribute to fuel failure and reactor shutdown. This understanding is crucial for advancing the nuclear energy industry. To unravel the intricacies of this complex phenomenon, a sophisticated high-fidelity finite element model of fuel rods was meticulously constructed. This model serves as the cornerstone for a computational analysis of flow-induced vibration responses utilizing ANSYS Batch, firmly roots in the principles of random vibration theory. In the pursuit of a profound understanding, support stiffness values to simulate and scrutinize diverse scenarios that fuel rods might encounter were systematically varied. The outcomes of these simulations were meticulously compiled to establish a comprehensive database of flow-induced vibration responses. This database stands as a valuable resource for future research endeavors and engineering applications within the nuclear energy domain. Taking a step forward, an innovative approach was adopted to streamline the analysis process. Leveraging the snapshot matrix derived from the extensive database, a high-fidelity reduced-order model (ROM) was developed. Two data-driven methodologies, namely proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) methods, were employed to construct the ROM. This ROM enables the rapid reconstruction of flow-induced vibration responses, facilitating efficient analysis and decision-making in real-world applications. A critical facet of this paper involves a comparative analysis of the reconstruction effectiveness of fuel rod flow-induced vibration responses using both the POD and DMD methods. The results of this comparison reveal that, when considering the reconstruction of vibration responses with the same number of modes, the POD method outperforms the DMD method. This finding underscores the importance of selecting appropriate methodologies based on specific objectives and computational efficiency. Furthermore, the results indicate that the DMD method excels not only in efficiently reconstructing the vibration responses of fuel rods but also offers the unique capability to assess the stability of each DMD mode. This dual functionality enhances the overall diagnostic capabilities of the DMD method, providing valuable insights into the dynamic behavior and potential instabilities of the fuel rod system. In conclusion, the exhaustive investigation outlined in this paper not only significantly contributes to the understanding of flow-induced vibration characteristics in fuel rods but also provides a robust framework for developing advanced models and methodologies for future research and practical applications in the nuclear energy industry. The comprehensive nature of our approach ensures that our findings are not only insightful but also applicable in shaping the future of nuclear energy technology.