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Purpose Vibrations are transmitted through the ground surface to building foundations, causing distress to structures and their occupants. Installing a wave barrier between the vibration source and buildings is a suitable method to mitigate ground vibration. However, the complexity arises in selecting the right trench design due to various influencing parameters. This paper aims to present a novel method to predict the efficiency of a geofoam-filled trench in mitigating ground vibrations within layered soil using an artificial neural network (ANN). Design/methodology/approach This study extends a parametric investigation conducted by Naghizadeh (Naghizadehrokni, 2022), where they identified key parameters influencing the trench’s efficiency. A multilayered feedforward neural network using the back-propagation training method was developed for the prediction task. The ANN model comprises input variables, including location, depth, width of the trench, thickness and shear wave velocity of the first layer as well as geofoam type. With a total of 18,750 data points from the parametric study, the network was trained and validated. Findings The accuracy of the trained model was evaluated using separate training, validation and testing data sets. Different neural network configurations were evaluated by comparing the coefficient of determination ( Opens in a new window. R2) and mean square error. The optimal architecture was used to predict previous results, revealing the accuracy and effectiveness of the ANN approach. Furthermore, the ANN’s predictive performance was compared with finite element model results. The results indicate a high level of accuracy, with a regression R-value of 0.98 for the regression analysis of the entire data set. Originality/value After studying previous research, the author identified a need for a prediction model to evaluate the efficiency of geofoam-filled trenches. To meet this requirement, an ANN model was developed using data collected from Naghizadeh (Naghizadehrokni, 2022) to precisely predict the performance of these trenches.