The global human resources (HR) landscape is currently undergoing a foundational paradigm shift, driven by the rapid integration of Artificial Intelligence (AI) and Machine Learning (ML). This research paper explores the transformative role of AI in recruitment and talent acquisition, moving from administrative automation to predictive strategic intelligence. By analyzing the recruitment lifecycle—from sourcing and screening to candidate engagement and final selection—this study deconstructs how AI algorithms optimize efficiency, reduce time-to-hire, and potentially mitigate (or inadvertently amplify) human bias.

Utilizing a Qualitative Systematic Literature Review (SLR) of academic studies and industry data from 2020–2026, the findings suggest that AI-driven recruitment can increase recruiter productivity by up to 40% and improve the quality of hire through data-driven matching. However, the study also highlights significant ethical challenges, including algorithmic transparency and data privacy. The research concludes that the most effective recruitment models of the future will be "Augmented Intelligence" systems, where AI handles high-volume processing while human recruiters focus on emotional intelligence, cultural fit, and strategic relationship building.

Keywords: Artificial Intelligence, Recruitment, Talent Acquisition, Machine Learning, Algorithmic Bias, HR Technology, Candidate Experience, Predictive Analytics.