A BP (Back-Propagation) neural network model optimized by PSO (Particle Swarm Optimization) and based on data preprocessing is proposed for sag prediction of overhead transmission lines, in order to solve the susceptibility of sag computation to measured data of temperature, wind speed, span and other parameters.For the missing data in collected database, the Synthetic Minority Oversampling Technique (SMOTE) was used to synthesize unbalanced samples.The proposed PSO-BP neural network was trained and tested by data obtained in different working environments.Experiments were carried out to verify the effectiveness of the proposed approach.The results showed that, compared with traditional BP neural network, the proposed model has a significant reduction in the relative error of sag prediction, and can accelerate the training speed as well as improve the sag prediction accuracy.