Groundwater pumping influences the rate of River-Aquifer (R-A) exchanges and alters the water budget of the aquifer. Therefore, fulfilling the total water demand of the area, with an optimal pumping rate of wells and optimal R-A exchanges rate, is important for the sustainable management of water resources and aquatic ecosystems. Meanwhile, comparison of the output of different simulation-optimization techniques, which is used for the solution of water resource management problems, is a very challenging task where different Pareto fronts are compared to identify the best results. In the present work, mathematical models were developed to simulate the R-A exchanges for the lower part of the River Ain, France. The developed models were coupled with optimization models in MATLAB environment and were executed to solve the multi-objective optimization problem based on the maximization of pumping rates of wells and maximization of groundwater input into the river Ain through R-A exchanges. The Pareto front developed by different simulation-optimization models was compared and analyzed. The Pareto fronts were juxtaposed based on the convergence, total diversity, and uniformity with the help of different performance metrics like hypervolume, generational distance, inverted generational distance, etc. The impact of different groundwater models based on domain size and boundary conditions was also examined. Results show the dominance of MOPSO over other optimization algorithms and concluded that the maximization of pumping rates significantly changes after considering the R-A exchanges-based objective function. It is observed that the model domain also alters the output of simulation-optimization, therefore the model domain and corresponding boundary conditions should be selected carefully for the field application of management models. ANN models were also developed to deal with the computationally expensive simulation model by reducing the processing time and were found efficient. Keywords: Simulation-Optimization, Multi-Objective optimization, Artificial Neural Network, River-Aquifer exchanges.