Abstract: Most existing flow regime prediction models in system analysis codes are derived from early experimental data, which restricts their applicability to a limited range of conditions. To leverage the expanding volume of experimental flow regime data, enhance model applicability, and improve prediction accuracy, this study compiled a comprehensive experimental dataset to establish a training database, followed by thorough data preprocessing. A two-phase flow regime prediction model was subsequently developed using artificial neural network algorithms and was benchmarked against traditional prediction models. The findings demonstrate that the newly developed model can be directly applied across a diverse array of operating conditions, offering superior prediction accuracy compared to conventional models. This study introduces a novel methodology for flow regime prediction, with the model’s applicability and accuracy poised to improve progressively as the training dataset is further expanded.