AI-DRIVEN OPTIMIZATION AND RISK MODELING IN STRATEGIC ECONOMIC ZONE DEVELOPMENT FOR MID-SIZED ECONOMIES: A REVIEW APPROACH

Abstract

This study investigates the integration of artificial intelligence (AI)-driven optimization and quantitative risk modeling in the planning and management of Strategic Economic Zones (SEZs) in mid-sized economies, focusing on measurable efficiency gains, resilience enhancement, and strategic value creation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 84 scholarly and applied studies—cited collectively 3,912 times—were systematically reviewed. AI applications, including predictive analytics, multi-variable optimization, and real-time monitoring, were shown to improve operational efficiency by 12% to 28%, while structured risk modeling reduced operational disruptions by over 15% and enhanced investor confidence, contributing to stronger foreign direct investment commitments. Thirty-eight studies on integrated frameworks demonstrated combined efficiency improvements of up to 35% and return-on-investment gains averaging 7% higher than non-integrated approaches. Geographic patterns indicated that 54% of the reviewed studies focused on mid-sized or emerging economies, demonstrating the adaptability of integrated AI-risk strategies to resource-constrained contexts. Temporal trends revealed rapid growth in interdisciplinary research over the past five years, reflecting the increasing recognition of these tools in economic zone governance. The findings confirm that AI-driven optimization and risk modeling, when applied systematically, provide a robust, data-driven foundation for improving SEZ operational performance, strengthening resilience against disruptions, and enhancing the competitive positioning of mid-sized economies in the global economic landscape.