Enterprise decision-making increasingly depends on the ability to integrate heterogeneous information, manage uncertainty, optimize resource allocation, and respond rapidly to dynamic operational conditions. Conventional information management approaches often treat information acquisition, decision analysis, and optimization as separate activities, limiting their ability to address complex and uncertain decision environments. This paper proposes a Metaheuristic-Driven Intelligent Information Management Framework for Enterprise Decision Optimization that conceptually integrates fuzzy information representation, dynamic network modeling, adaptive decision mechanisms, and metaheuristic optimization. The framework is theoretically positioned using the supplied literature on fuzzy optimal allocation, dynamic network flow, emergency evacuation decision-making, human behavioral uncertainty, and route optimization. Although the referenced studies primarily address emergency evacuation and transportation-related decision environments, their underlying principles—dynamic optimization, fuzzy constraints, uncertain behavioral responses, collective decision-making, and resource allocation—are transferable to enterprise information management. The proposed methodology consists of information acquisition, uncertainty modeling, decision-network construction, metaheuristic search, adaptive evaluation, and decision-support generation. Analytical findings indicate that combining fuzzy representations with dynamic optimization can improve the robustness of enterprise decisions under uncertain conditions, while metaheuristic search can address large combinatorial decision spaces more flexibly than rigid deterministic approaches. The framework contributes a conceptual bridge between intelligent information management and optimization-driven enterprise decision support, while recognizing limitations associated with computational complexity, parameter sensitivity, and the absence of empirical enterprise datasets. The proposed framework provides a foundation for future simulation-based and real-world validation.
