We present a novel method and mathematical model using four artificial intelligence AI algorithms to anticipate the cumulative degree of soil compaction CDSC after dynamic compaction DC. Four AI algorithms adopted in this study include support vector regression SVR, artificial neural network ANN, random forest RF, and gradient boosting machine GBM. Input variables for AI algorithms involve the average SPT N value Nini before dynamic compaction, cumulative applied energy normalized with a cross-sectional area of tamper Ea, and the number of the tamper drops Ndrops. Apart from cross-validation with a testing set, additional in situ test data compiled from a different section within the studied site are used to estimate the generalized capacity of the AI models. In addition, we conduct out-of-distribution analyses for the four AI algorithms in view of parametric studies. The CDSC prediction performance for the four AI models results in high prediction metrics of accuracy with the r2 higher than 0.9 for the testing scenario while the r2 of the other AI models is more than 0.9 when out-of-sample data are considered except for the GBM. The ANN seems to be the best model as the parametric study considers out-of-distribution data and suggests a strong relationship between input variables and CDSC that is more coherent with engineering principles for DC. Finally, the ANN model is utilized to develop a new mathematical model for CDSC prediction.