<![CDATA[The rapid growth of urban vehicle ownership has intensified traffic congestion and decreased mobility efficiency, particularly at unsignalized intersections, which remain common in developing countries. These intersections, lacking automated control mechanisms, often experience high delays, long queues, and increased accident risks. This study conducts a Systematic Literature Review (SLR) to synthesize research developments on traffic engineering management models aimed at optimizing unsignalized intersections using multi-objective analysis. Following the PRISMA framework, 48 peer-reviewed articles published between 2013 and 2026 were systematically analyzed from major databases such as Scopus, ScienceDirect, IEEE Xplore, and Google Scholar. The findings reveal a significant evolution from single-objective to multi-objective optimization approaches, emphasizing trade-offs between efficiency, safety, and environmental sustainability. The most frequently applied algorithms include NSGA-II, PSO, and Fuzzy-MCDM, often integrated with simulation tools like VISSIM and SIDRA Intersection. Recent trends indicate growing adoption of AI-driven and machine learning-based hybrid models, enabling adaptive and real-time traffic management. However, research gaps remain, particularly regarding model validation under mixed traffic conditions and real-world implementation using IoT-based traffic data. This study contributes to both theory and practice by providing a structured overview of optimization frameworks and identifying future research directions toward developing adaptive, data-driven, and context-sensitive models. The results serve as a foundation for designing intelligent traffic management systems that improve the performance and safety of unsignalized intersections while supporting sustainable urban mobility.]]>
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