<![CDATA[The development of Artificial Intelligence (AI) technology is currently experiencing massive acceleration, particularly in the fields of Computer Vision and Genetic Algorithms. This study aims to analyze the trends, methodologies, and implementation effectiveness of both technologies in solving system optimization and digital image analysis problems across various sectors, including industry, food, education, social, and information systems. The method employed in this research is a Systematic Literature Review approach analyzing 12 indexed research documents and textbooks published between 2014 and 2025. The analysis was conducted by classifying the research materials into two main clusters: the image analysis cluster (Computer Vision) and the heuristic search-based decision optimization cluster (Genetic Algorithm). The results indicate that the integration of modern Computer Vision, such as the YOLO architecture and Raspberry Pi graphics processors, successfully enhances object detection accuracy, vegetable freshness classification based on leaf texture, SIBI sign language translation, and real-time milkfish size sorting automation. On the other hand, Genetic Algorithms have proven effective in solving complex combinatorial optimization problems (NP-hard problems), such as ICT training scheduling, competence-based student thesis topic recommendations, and the development of career selection expert systems with a high level of solution convergence. The conclusion of this study underscores that the combination of precise image analysis and adaptive system optimization plays a crucial role in driving intelligent automation in the modern era.]]>
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