Metaheuristic Optimization Review MOR 3066-280X 10.54216/MOR https://www.americaspg.com/journals/show/4199 2024 2024 School of international languages Zhengzhou University, Henan, China Asifa Asifa Groundwater sources can significantly meet the agricultural, industrial, and domestic demands especially in the arid and semi-arid areas. Nonetheless, ground water has depleted and its quality has declined greatly due to over-pumping, climate fluctuation and ever-growing population pressure. High quality modeling and optimiza-tion techniques that are able to address the complexity and uncertainty of the groundwater system are needed to efficiently manage and provide sustainable use of these resources. In many cases whenever handling nonlin-earity, high dimensionality and multiple competing objectives properties of many groundwater problems, the traditional deterministic or gradient based methods are insufficient. In this respect, metaheuristic optimization algorithms have become an effective tool in groundwater management tasks in general. This paper will show a detailed usage of metaheuristic optimization methods to solve some important problems in ground water mod-eling and management such as well location, optimal pumping rate optimization, ground water contamination, and aquifer parameter estimation. Metaheuristics such as Genetic Algorithms (GA), Particle Swarm Opti-mization (PSO), Differential Evolution (DE), and Ant Colony Optimization (ACO) have demonstrated their effectiveness in exploring large and complex search spaces and avoiding local optima. These algorithms are combined with computer modeling of groundwater flow and transport (e.g., MODFLOW and MT3DMS) so as to simulate the dynamics of the system and test solutions generated by the algorithms iteratively, and within a feedback environment. The hybridization of metaheuristic methods with surrogate modeling approaches, including artificial neural networks (ANNs) and support vector machines (SVMs), is also explored to reduce computational burdens associated with repeated model evaluations. By integrating optimization algorithms together with data-driven models, the framework produces a tradeoff between the accuracy of the solution and efficiency o f c alculation. I n a ddition, multiple o bjective o ptimization is a pplied i n o rder t o h ave trade-offs between competing objectives e.g. minimizing cost and maximizing aquifer sustainability or minimizing the contaminant spreading and maximizing water delivery. To illustrate the generality and validity of the suggested method, a real-word example of an aquifer system is applied. Findings reveal that metaheuristic approaches are better alternatives to conventional methods regarding the quality of solution, the rate of convergence, and the flexibility to uncertain or incomplete d ata. The framework has the potential of providing the optimized man-agement methods that can help the decision-makers come up with such policies that can be acted upon where the use of groundwater will be sustainable. On balance, the current study informs the current knowledge on intelligent water resources management by ensuring that the powerflexibility of metaheuristic optimization in groundwater context goes into record. The results provide a clear rationale in why synergizing computational intelligence with hydrological science to a groundwater sustainability challenge is important. 2026 2026 26 43 10.54216/MOR.050102 https://www.americaspg.com/articleinfo/41/show/4199