<p>This study utilizes three machine learning (ML) models, namely artificial neural network (ANN), Gaussian process regression, and adaptive boosting, to predict the efficiency factor of a strip footing resting on layered soil. 400 datasets with six input variables, including the angle of shearing resistance of the upper and lower soil layer, the unit weight of upper and lower soil layers, the depth of the footing, and the thickness of the upper soil layer, were used to compute the output. To legitimize the precision of these ML models, different performance parameters were used. These performance parameters include coefficient of determination (R²), variance account factor, performance index, root mean square error (RMSE), mean absolute error, and maximum absolute error. Results showed that the ANN model had the most suitable outcomes among these three ML models, with the highest R² = 0.988 and the lowest RMSE = 0.02 in the training phase, and R² = 0.964 and RMSE = 0.035 in the testing phase, respectively. The first-order second moment technique was used to compute the reliability index. The efficacy of the models was also checked by using score analysis, regression plots, William’s plots, external validation, and comparative analysis. Sensitivity analysis was carried out to check the influence of each input parameter on the output.</p>