tThe seawater greenhouse desalination technology is a kind of desalination plant which simulates thewater cycle through seawater evaporation and condensation into freshwater. A novel random vectorfunctional link (RVFL) network integrated with artificial ecosystem-based optimization (AEO) algorithmis proposed to predict the performance of the seawater greenhouse (SWGH) system. Power consumptionand water productivity of the SWGH are predicted using the proposed RVFL-AEO model. The statisticalanalyses using different statistical criteria such as root mean square error, mean absolute error, meanrelative error, efficiency coefficient, coefficient of determination, overall index, and coefficient of residualmass are also carried out to examine the efficiency of the proposed neural network. The statistical toolsobtained a perfect match between the experimental and the proposed model results. The performanceof the RVFL-AEO model is compared with that of the conventional RVFL model. RVFL-AEO showed abetter performance compared with RVFL; which indicates the role of AEO in obtaining the optimal RVFLparameters that enhances the accuracy of the model.