<![CDATA[The advancement of Artificial Intelligence (AI) has accelerated the adoption of Decision Support Systems (DSS) to assist managerial decision-making in the increasingly complex and dynamic hospitality industry. This study aims to systematically examine how AI-based DSS are utilized to support managerial decision-making in the hospitality sector, with a particular focus on the types of decisions supported, the AI techniques employed, the benefits obtained, and the challenges of implementation. This research adopts a Systematic Literature Review (SLR) approach guided by the PRISMA framework. A comprehensive literature search was conducted using the Scopus database, resulting in 32 peer-reviewed journal articles that met the inclusion criteria within the publication period of 2017–2026. The findings indicate that AI-based DSS are predominantly used to support operational and tactical decisions, particularly in demand and occupancy forecasting, dynamic pricing and revenue management, workforce scheduling, and service quality management. Machine learning and predictive analytics emerge as the most widely applied AI techniques, while rule-based systems are used to a more limited extent. The literature also highlights key benefits, including improved decision accuracy, enhanced operational efficiency, and better service quality. However, these benefits are accompanied by challenges related to data quality, system transparency, and organizational readiness. This study provides a structured synthesis of the role of AI-based DSS in managerial decision-making within the hospitality industry and offers a foundation for future research and managerial practice.]]>
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