<![CDATA[This study examines the role of artificial intelligence in enhancing risk-based decision-making across multiple sectors. The rapid adoption of AI has transformed organizational approaches from reactive to proactive risk management; however, gaps remain in governance, ethical readiness, and real-world implementation. Therefore, this study aims to systematically review the application of AI, identify its benefits and challenges, and analyze the relationship between AI capabilities, risk management processes, and decision outcomes. This research employs a systematic review method guided by the PRISMA framework, analyzing recent literature published between 2023 and 2025 from major academic databases. The findings indicate that AI significantly improves predictive accuracy, operational efficiency, and decision quality through advanced data processing techniques such as machine learning and deep learning. AI applications are widely observed in healthcare, finance, and environmental risk management, demonstrating strong performance in prediction and analysis. However, challenges related to data quality, model transparency, ethical risks, and governance limitations persist. This study concludes that AI should be implemented as a socio-technical system supported by robust governance frameworks and human oversight to ensure responsible, transparent, and sustainable risk-based decision-making.]]>
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