Local scour around a vibrating submarine pipeline threatens its structural stability and requires accurate predictive models to ensure its safety. This study evaluates the performance of three standalone Machine Learning (ML) models -- M5 model trees, Adaptive Robust Regression (ARR), Support Vector Regression (SVR), and an ensemble Gradient Boosting (GB) model on scour prediction. Their predictive accuracy is compared against four empirical formulas using an experimental dataset and statistical performance metrics. The results indicate that GB outperforms all other models, achieving the highest r2 and lowest RMSE and MAPE in the training and testing phases. M5 and SVR show moderate accuracy, while ARR exhibits the weakest performance. Empirical equations perform poorly, often significantly overestimating or underestimating scour depth, demonstrating the limited generalization. Correlation analysis highlights that vibration amplitude is the dominant factor. These findings emphasize the superiority of ensemble learning over standalone ML models and empirical equations for improving scour depth predictions.

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