This article presents the development and implementation of an urban water planning decisionsupport system that integrates fuzzy logic techniques and a set of 25 metaheuristic algorithms. The model is designed to operate under an environment characterised by multiple operational, environmental, regulatory, and infrastructure constraints, which are incorporated through dynamic penalties in the objective function. Fuzzy logic is used to transform imprecise critical variables, such as source availability, sectoral demands, and operating costs, into CRISP inputs usable by optimisation algorithms. The methodology allows for the evaluation and comparison of the performance of each heuristic in terms of computational efficiency, solution quality, and feasibility under realistic urban conditions. The results show significant differences in accuracy, robustness, and convergence times, providing a quantitative basis for the selection of sustainable, resilient, and adaptive water management strategies in the context of smart cities. Finally, future lines of research are proposed, focusing on algorithmic hybridisation, the incorporation of Explainable Artificial Intelligence (XAI), and integration with real-time water governance platforms.