<![CDATA[Decision Support Systems are critical tools in enhancing decision-making across various industries, providing data-driven insights to guide complex choices. Traditional support system , however, often face challenges related to uncertainty, complexity, and adaptability. This paper explores the integration of Hybrid Machine Learning (ML) and Operations Research (OR) models as a solution to these limitations. ML techniques, such as predictive analytics and deep learning, enable data-driven pattern recognition, while OR methodologies, including optimization and stochastic modeling, offer structured problem-solving approaches. By combining these paradigms, the proposed hybrid model aims to improve decision accuracy, resource allocation, and problem-solving efficiency.in addition, Real-world case studies in healthcare, supply chain management, finance, and transportation demonstrate the effectiveness of this hybrid approach in optimizing decision-making processes. A comparative analysis of hybrid ML-OR models with traditional DSS highlights significant improvements in computational efficiency, accuracy, and adaptability. This research underscores the potential of hybrid ML-OR frameworks to drive more intelligent, robust, and scalable decision support solutions for a wide range of applications.]]>
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