Due to the harsh operating environment, the corrosion problem of gathering and transportation pipelines has become increasingly severe, necessitating the development of an accurate model to predict the internal corrosion rate of such pipelines. Herein, a pipeline corrosion prediction method that combines physics-guided neural networks (PGNN) with an improved particle swarm optimization (IPSO) algorithm is proposed. By analyzing the electrochemical corrosion mechanism, the universal impact of variations in different corrosion factors on the corrosion rate is summarized. Subsequently, based on these universal laws, a loss function characterization method is proposed to construct a physics-guided internal corrosion rate prediction model. The model's hyperparameters are optimized using a particle swarm algorithm improved through reverse learning, nonlinear weight adjustment mechanisms, and cosine algorithm enhancements. Finally, the predictive model is subjected to interpretability analysis using electrochemical corrosion mechanisms, partial dependence plots, and the SHAP algorithm. The accuracy of the model is evaluated using four metrics: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R²), while its reliability is assessed based on its consistency with corrosion mechanisms. Results demonstrate that the PGNN-IPSO method can avoid learning erroneous patterns contradicting physical principles, thereby enhancing prediction accuracy. This research holds significant implications for corrosion protection, reliability assessment, and maintenance decision-making regarding gathering and transportation pipelines.