Accurate and meaningful prediction of saltwater intrusion in coastal aquifers requires appropriate prediction tools. Artificial intelligence-based prediction models and their ensembles have been a better choice for mimicking the complex and nonlinear seawater intrusion progressions in coastal aquifers. This study utilizes a weighted-average ensemble of ‘heterogeneous’ prediction models to predict the saltwater intrusion progression in a coastal aquifer study area. The Dempster-Shafer theory of evidence is employed to calculate the weights of five different prediction model algorithms. Corresponding weights for individual prediction models are utilized in developing the ensemble prediction. Ensemble prediction performance for salinity intrusion in coastal aquifers in this effort is evaluated using several descriptive metrics. The values of the descriptive metrics suggest that the ensemble model performs in the same way as the best model in the ensemble. The methodology is evaluated for an illustrative coastal aquifer study area exposed to pumping-induced saltwater intrusion.