This narrative literature review explores the role of artificial intelligence (AI) and data analytics in enhancing recruitment management within the U.S. hospitality industry. Amid persistent labor shortages and high turnover, hospitality employers are increasingly adopting AI-enabled tools to streamline hiring processes, improve candidate engagement, and support data-driven decision-making. Drawing on peer-reviewed studies, industry reports, and policy documents published between 2018 and 2025, this review synthesizes empirical and conceptual insights across key thematic areas: AI-driven sourcing and screening, chatbot-enabled candidate engagement, predictive and prescriptive analytics, algorithmic fairness, implementation barriers, and regulatory compliance. Findings indicate that AI tools significantly improve recruitment speed and efficiency, particularly for high-volume, low-complexity roles. Chatbots enhance candidate experience and reduce recruiter workload, while predictive models offer potential for workforce planning and attrition forecasting. However, the benefits are unevenly distributed, with larger firms achieving greater returns due to superior data infrastructure and vendor partnerships. Critical concerns persist around algorithmic bias, transparency, and auditability, especially in decentralized hiring environments. The review offers practical recommendations for Human Resource (HR) leaders, including rigorous vendor evaluation, pilot testing with measurable Key Performance Indicators (KPIs), and alignment with ethical and legal standards such as the NIST AI Risk Management Framework. Theoretically, it contributes to bridging strategic human resource management with digital technology adoption frameworks. Limitations include reliance on published sources and the rapid pace of technological change. Future research should prioritize longitudinal case studies, independent audits, and exploration of candidate perceptions to inform responsible and effective AI integration in hospitality recruitment.