This survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As AI continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. The article examines methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based DSSs. It aims to guide researchers, practitioners, and decision-makers by presenting a methodological taxonomy of XDSSs and discussing representative works from different application fields such as healthcare, transportation, manufacturing, finance, and education. The analysis reveals growing interest in applying XDSSs to improve accuracy, transparency, and compliance while boosting user confidence and trust. The article concludes by highlighting the importance of XAI in enhancing the usability and acceptance of AI-driven systems and outlines future research directions.This survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As AI continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. The article examines methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based DSSs. It aims to guide researchers, practitioners, and decision-makers by presenting a methodological taxonomy of XDSSs and discussing representative works from different application fields such as healthcare, transportation, manufacturing, finance, and education. The analysis reveals growing interest in applying XDSSs to improve accuracy, transparency, and compliance while boosting user confidence and trust. The article concludes by highlighting the importance of XAI in enhancing the usability and acceptance of AI-driven systems and outlines future research directions.