The integration of wind-solar hybrid systems presents a transformative pathway to bolster renewable energy resilience, yet their optimal deployment in geographically diverse environments remains hindered by spatiotemporal intermittency, conflicting stakeholder priorities, and dynamic ecological constraints. This study proposes a novel fuzzy meta-goal programming (FMGP) framework to reconcile techno-economic, environmental, and social objectives in wind-solar hybridization, with a focus on arid and coastal ecosystems—regions characterized by contrasting meteorological volatility and land-use sensitivities. By embedding fuzzy set theory into meta-goal structures, the model quantifies uncertainties in renewable resource availability (e.g., wind shear variability, solar irradiance fluctuations) while balancing antagonistic criteria such as levelized energy cost minimization, carbon footprint reduction, and biodiversity preservation. The FMGP approach uniquely incorporates synergistic complementarity metrics to exploit temporal offsetting between wind and solar generation cycles, enhancing grid stability in resource-erratic zones. Empirical validation through case studies in a hyper-arid desert and a storm-prone coastal region reveals Pareto-optimal solutions that achieve up to 23% improvement in annual energy yield reliability and 18% reduction in land-use conflicts compared to conventional multi-objective models. Furthermore, the framework introduces a stochastic acceptability index to evaluate policy robustness under climate change scenarios, demonstrating adaptive capacity in mitigating energy-water nexus pressures in arid areas and storm resilience trade-offs in coastal grids. This research advances sustainable hybrid system design by harmonizing multi-scale environmental governance with precision energy planning, offering policymakers a decision-centric tool to navigate the socio-ecological complexities of the renewable transition.