Freshwater aquifers in coastal zones are vulnerable to seawater intrusion (SWI). SWI is a natural and troublesome phenomenon that impairs the potability of groundwater. Moreover, impacts of population growth and climate change such as recharge variations, sea level rise (SLR), and land-surface inundation (LSI) associated with SLR may exacerbate this problem. Sea levels are expected to rise substantially due to climate change. It is widely assumed that this rise will adversely affect SWI processes in coastal aquifer. Mitigation measures can be either by changing the water resources management legislation or by implementing physical or hydraulic barriers. The focus of this project is on assessing the effectiveness of a novel hydraulic barrier as a mitigation measure, in addition to addressing the challenges of two hydraulic barriers to control SWI in coastal aquifers. The decision makers should be informed of the risks surrounding their decisions before implementing any mitigation measure. A decision support tool is developed to determine whether an investment is needed, and the associated degree of uncertainty. A generalized computationally efficient framework is proposed to analyze the predictive uncertainty of models. The novel components of the framework include efficient parameter space sampling using an optimized Latin hypercube sampling strategy, and applying the Null Space Monte Carlo method (NSMC) along with a developed filtering technique. The NSMC renders generated sample sets to calibrate the model while exploring the null space. This space contains parameter combinations that are not sufficiently supported by observations. The filtering technique omits low-potential parameter sets from undergoing model calibration. The framework is tested on the seawater intrusion (SWI) model of Wadi Ham aquifer, to investigate aquifer sustainability in 2050. It is concluded that, with a moderate to a high degree of certainty, SWI threatens the main pumping fields, and this would adversely affect the suitability of groundwater for irrigation. Therefore a managed aquifer recharge (MAR) scheme involving an infiltration pond is used in this study to mitigate SWI caused by future climate change in 2050. However, running a management model, based on variable-density groundwater flow and solute transport equations, is time-consuming. Besides, optimizing the model objectives would require several simulation runs. To reduce the computational burden, a surrogate-assisted simulation-optimization framework is developed, based on constrained multiobjective Bayesian optimization (BO) algorithm. BO is a data-efficient learning technique, which solves computationally expensive problems with few iterations. This algorithm is introduced to SWI management for the first time in this study. The proposed framework is applied to determine the optimal location and dimensions of an infiltration pond considering environmental and economic effectiveness. Since BO is newly introduced in this field, it was benchmarked against the widely used robust NSGA-II (Non-dominated Sorting Genetic Algorithm II) method. The results prove the effectiveness of BO in achieving the optimum design parameters of the mitigation measure in much fewer simulation runs. Mixed hydraulic barriers, as another mitigation measure, is optimally designed using the BO approach. Through evaluating several management scenarios, it is shown that the injection has a significant impact on the management, while the abstracted water provides an alternative source of water. A sensitivity analysis is conducted on the optimization problem to illustrate its efficiency by omitting the barriers one at a time and assessing impacts on the objective and constraint functions. A third novel mitigation measure is introduced in this research as an improvement for the potential loss in the available freshwater induced by the negative barriers in the mixed hydraulic barriers method. This measure combines the injection of reclaimed water with the use of groundwater circulation wells (GCW), which creates a sustainable solution (Inj_GCW mitigation measure). First, an illustrative simplified unconfined coastal aquifer is used to quantitatively evaluate the Inj_GCW's performance in controlling SWI. Using the findings from this aquifer, recommended design parameters are estimated for a field-scale case study of the Nile Delta aquifer in Egypt. The study adopts a 2100 future scenario that considers Sea Level Rise due to climate change and projected population growth. The results of implementing the Inj_GCW measure on the Nile Delta aquifer show retardation in the SWI compared to the expected intrusion in 2100, and a reduction in the aquifer salinity. At the well injection screen of the GCW, a brackish water bubble is formed acting as a hydraulic barrier.