An effective application of artificial intelligence involves artificial neural networks. Artificial neural networks and linear regression models were developed to simulate the effects of using discarded ceramic waste as a subgrade for pavement. The ceramic waste was used at 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. A sample with 0% ceramic waste was tested to serve as a reference sample. The dataset was produced from laboratory experimentation findings used to train, test, and evaluate the model. A training set, a target set, and a prediction set were created from the dataset. The artificial neural network MSE was 0.42-1.40, while the linear regression model range was 1.74 to 3.63 for ceramic modified samples. The R2 2 range for the ANN model was 0.85-0.92, and the linear regression model exhibited a range of 0.71-0.78. The ANN model was more accurate than the linear regression model. Future studies are required to compare different machine-learning approaches for predicting soil mechanical properties.
