<![CDATA[Decision-making in domains such as healthcare, finance, and smart systems demands frameworks that combine model-driven expertise with data-driven adaptability. This paper proposes a hybrid decision support framework that integrates Explainable AI (XAI) with multi-criteria optimization to enhance transparency, robustness, and adaptability. Unlike traditional systems, our approach unifies mechanistic models with machine learning and embeds interpretability and optimization mechanisms. Comparative evaluation against state-of-the-art methods shows consistent performance gains, achieving 15–25% lower error rates compared with data-driven baselines and generating more diverse Pareto-optimal solutions. These improvements highlight the framework’s potential as a reliable, explainable, and scalable solution for complex, real-world decision-making]]>
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