For the application of machine learning to sound source localization, much train data distinguished from test data is needed to build the machine learning model. In Shallow-water Acoustic Variability Experiment (SAVEX-15) conducted in shallow water (water depth ∼100 m) in Northern East China Sea (ECS), ship noise of the R/V Onnuri was recorded by two vertical line arrays. Acoustic data of ∼80% was applied to the training dataset and the others having different trajectories were used for the test data. The recorded data is preprocessed by a sample covariance matrix and it is used as the input data of the machine learning model: Feedforward neural network (FNN) and support vector machine (SVM). The results by FNN and SVM will be discussed with conventional localization method using ray-based blind deconvolution (RBD) and array invariant (AI). [Work supported by the Agency for Defense Development, Korea (UD210004DD).]