This paper proposes a comparison of various machine learning models used for fluid hammer pressure surge vulnerability assessment in seawater pipeline in the LNG regasification plant. One of the most critical components of natural gas production system is the Liquefied Natural Gas (LNG) regasification system which converts the LNG back from liquid phase to gaseous phase (Natural Gas) using a complex pipeline network carrying seawater to extract cold energy from the LNG which is susceptible to fluid hammer formation. In this paper, machine learning based methodology is presented to predict fluid hammer effect in the fluid flow in the complex pipeline network of LNG regasification system. The methodology consists of two parts: first, this study includes a simulation-based model developed in Aspen HYSYS to find the design parameters affecting the water hammer effect in the LNG regasification system pipeline based on the Design of Experiments principles and second, various machine learning algorithms are proposed to predict the vulnerability of the pipeline due to water hammer effect in the pipeline. The Support Vector Machine algorithm with radial kernel was found to be the best model to predict the vulnerability of the pipeline. © 2022 IEEE.