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This study presents a comprehensive review and comparative synthesis of the significant progress achieved in fuzzy optimization between 2020 and 2025. It examines the evolution of algorithms, models, and application domains that collectively mark the transition from classical mathematical programming to intelligent, hybrid, and adaptive optimization paradigms. The integration of fuzzy logic with artificial intelligence (AI), the Internet of Things (IoT), and blockchain technologies has improved the transparency, flexibility, and resilience of complex decision-making frameworks [6], [7], [14], [15], [23]. Empirical evidence from fields such as sustainable supply chains, renewable energy management, and autonomous control underscores the increasing maturity and industrial relevance of multi-objective fuzzy models for handling uncertainty and nonlinearity [4], [15], [17], [20], [23]. The review emphasizes the prominence of hybrid metaheuristic–fuzzy algorithms, which demonstrate superior convergence and robustness in solving large-scale, nonlinear ptimization problems [4], [22]. Nevertheless, unresolved issues persist concerning scalability, interpretability, and real-time adaptability [6], [14], [15], [20], [22]. Future research directions include the convergence of fuzzy reasoning with deep and reinforcement learning, the exploration of quantum–fuzzy hybrid models, and the development of sustainability-oriented, interpretable decision frameworks [17], [22], [23]. Collectively, fuzzy optimization is advancing toward a human-centric and explainable computational paradigm, capable of enabling intelligent and trustworthy decision systems under uncertainty.