Abstract: This research aims to develop a novel creep constitutive model, leveraging neural networks to precisely predict long-term creep behavior from short-term data. The new types of nuclear reactors require higher design temperatures and longer service life than before. Consequently, the investigation of the long-term creep in metallic components utilized in high temperature environments gains greater importance. The analysis of creep necessitates precise constitutive models which need a substantial quantity of long-term experimental data. However, the acquisition of such data through experiments incurs considerable costs, thus the need is highlighted to develop a dependable model that can forecast the long-term creep behavior of materials based on short-term creep test data. What’s more, traditional approaches often struggle with the precision required to capture the nonlinear deformation mechanisms of the primary creep, as well as difficulties in parameter calibration. At present, the amalgamation of artificial neural network (ANN) techniques in the creep of reactor design lacks any established precedent. In this paper, an ANN model that characterizes the creep deformation properties of materials and predicts the long-term creep deformation behavior was presented. This approach eliminates the need to prematurely define a cut-off point between the primary and secondary creep stages, which can lead to overestimating the slope of the second stage and introduce challenges in engineering design. Other key highlight of this research is the introduction of a machine learning-based method to identify the cut-off point without compromising the accuracy of the primary creep stage prediction. This approach ensures that the model captures the true creep behavior throughout all stages, resulting in more reliable predictions of material performance and lifetime. After identifying the cut-off point between the primary and secondary creep stages, neural networks were used for fitting in the primary creep. This allowed the model to capture the intricate deformation patterns with high precision. For the secondary creep, the neural network was employed to perform reverse parameter calibration of the Norton Bailey’s power law. The above approach ensures that the model captures the steady-state creep behavior accurately, without compromising the accuracy of the primary. The analysis indicates that the optimized creep constitutive model can describe the creep behavior of materials more accurate than either of the creep theory or neural network training model. The model predicts the long-term creep features with an improved accuracy. Furthermore, the ability to accurately predict creep behavior based on short-term data holds immense potential for applications in material performance evaluation and lifetime prediction.