The major objective of the study is to make exergy analysis of natural gas liquid (NGL) process more understandable by coupling it with the use of an artificial neural network modeling. The presented method permits to provide an energy diagnosis of the process under a wide range of operating conditions. As a case study, Siri Island NGL Recovery in Iran is considered. The Aspen Plus process simulator linked with MATLAB Software was used to obtain thermodynamic properties of the process streams and to perform exergy balances. The results are validated with industrial data. The exergy destruction and exergetic efficiency for the main system components and for the entire system were calculated. Major sources of irreversibility in the process are identified, and the best conditions for process improvement are presented. The simulation results reveal that the exergetic losses of the separation towers, heating/cooling equipment, and compression/expansion section obtain the highest rank among the other components of the plant. The results show that the overall exergetic efficiency of the system is about 61%. After proposing new operational conditions, another exergetic analysis was made that caused a decrease of 6% in the exergetic losses of the entire system. Then, the recorded and calculated data are used as inputs for the neural network. The results show that ACO R is highly effective to optimize the performance of the neural networks to predict overall exergy efficiency. This method was compared with other current methods, and the results indicated that the integrated n Ant Colony Optimization-Back Propagation (ACO R -BP) provides the least error on the testing dataset.