The strategic allocation of marine oil resources involves complex trade-offs between economic performance, operational efficiency, and environmental sustainability—often under significant uncertainty. This paper develops a robust decision-support framework that integrates Fuzzy Logic with Nonlinear Goal Programming (NLGP) to address the multi-objective optimization problem inherent in marine oil extraction and resource allocation. Uncertainty in key parameters—such as extraction costs, production yields, and environmental impact limits—is modeled using fuzzy sets, enabling a more flexible representation of real-world ambiguity. The model simultaneously optimizes multiple nonlinear and conflicting goals, including profit maximization, cost minimization, and ecological risk reduction. To efficiently solve the resulting nonlinear programming problem, a hybrid solution approach is proposed that combines fuzzy goal programming techniques with metaheuristic optimization, specifically a tuned Genetic Algorithm. The framework is applied to a representative offshore oil field scenario, demonstrating superior performance in solution quality and robustness compared to traditional linear and crisp optimization methods. The results underscore the potential of fuzzy NLGP models in supporting high-stakes operational decisions in uncertain and dynamic environments. This work contributes to the growing body of operations research methods that address multi-criteria decision-making under uncertainty, with direct implications for energy resource planning and sustainable marine operations.