Abstract: In the present study, a data-driven neural network methodology was proposed for the rapid prediction of multi-physics interactions in heat pipe reactors, and grounded in a comprehensive framework of multi-physics coupling. By leveraging neural networks, certain numerical computation modules within the framework were effectively replaced, which facilitated an efficient resolution of the iterative process inherent to the nuclear-thermal-mechanical multi-physics coupling. In the process of multi-physics coupling, there exists a sophisticated iterative mechanism, wherein the computational analyses of distinct physical domains largely function autonomously. When contemplating the integration of neural network paradigms as surrogates within the multi-physics coupling framework, it is pivotal to target the numerical computations intrinsic to individual physical domains. By deploying one or multiple neural network models as substitutes, this methodology not only sustains the scalability inherent to rapid predictive schemes but also attenuates the complexity associated with the neural network’s surrogate functionalities. Concurrently, this approach necessitates a reduced dataset for training, and through the amalgamation of diverse neural network models, markedly augments the adaptability of the overarching rapid predictive framework. Furthermore, multiple neural networks, operating autonomously, can be dynamically calibrated with respect to their training data volumes based on their predictive performance. This not only optimizes their predictive accuracy but also facilitates the cost-effective expansion of training datasets in accordance with specific model requirements. Taking “Megapower” as the subject of analysis, pertinent neural network models were constructed and employed for rapid predictive parameter estimation, with the results juxtaposed against traditional numerical computations. The discrepancies between the key parameters yielded by rapid predictions and those from numerical simulations were minimal. Specifically, the maximum stress differential does not exceed 2 MPa, and the average fuel temperature difference is less than 3 K. Notably, while numerical computations demand six hours of processing time, the rapid prediction on the same platform requires a mere 4 minutes, marking a reduction in computational time by over 95%. Given these outcomes, it can be posited that neural network-based rapid prediction schemes exhibit high precision and accelerated processing speeds. Coupled with their inherent flexibility and modest training data requirements, and the capacity for targeted model optimization, it is advocated that neural network-based rapid prediction serves as a preferred methodology for addressing challenges such as core optimization in scenarios that demand large-scale computations. Concurrently, the neural network models utilized in rapid predictions have less stringent computational resource requirements and offer greater adaptability to varied environments. Capitalizing on these attributes, in conjunction with contemporary compact mobile reactors, one can facilitate on-site deployments with relative ease. Such integrations pave the way for prompt alerts in accident scenarios, thereby enhancing the safety of the reactor core.