The Xijiang oil field subsea pipeline frequently shuts down the platform during the high typhoon season, and its crude oil has high pour point and high wax appearance temperature characteristics. After suspension of transport, due to the decrease in pipeline temperature, wax deposition and pipe clogging incidents are likely to occur, which seriously threaten the safety of the pipeline system. A transient simulation software was used to calculate the effects of displacement flow rate, suspension time, pipeline outlet pressure, crude oil transportation temperature, ambient temperature, and pipeline throughput on the maximum displacement transient pressure. The results indicated that the displacement flow rate has the most significant impact on the maximum displacement transient pressure. Support vector machine (SVM), random forest (RF), and BP neural network methods were used to train the results of the transient software simulation. A comparative analysis was conducted using MSE and R2 evaluation indicators. The model based on the quadratic SVM demonstrated the best performance in terms of prediction accuracy and generalization capability. Additionally, a supporting software interface was developed, which can be used for rapid prediction of the pipeline's maximum transient pressure variation characteristics under different displacement conditions, allowing for the reasonable optimization of operating parameters and ensuring the safe and efficient execution of pipeline displacement operations.