Artificial Intelligence (AI) has fundamentally transformed Human Resource Management (HRM) by shiŌing talent acquisiƟon from manual administraƟve screening to predictive, data-driven selection architectures. This research paper evaluates the empirical impact of AI-enabled recruitment and selecƟon tools on hiring efficiency, candidate experience, and algorithmic fairness. Analyzing longitudinal HR benchmark data across Indian corporate enterprises from FY 2020-21 to FY 2024-25, alongside forecast trajectories through FY 2026-27, this study examines core indicators including the AI Recruitment IntegraƟon Score, Hiring Efficiency Rate (time tohire reduction), Candidate Experience Score (CES), and the Algorithmic Fairness Index. Empirical findings demonstrate that organizations scaling AI recruitment adoption from 42.0% to 86.5% achieved a proporsonal surge in Hiring Efficiency from 28.5% to 64.0% and elevated CES from 38.0% to 74.5%. Pearson correlaƟon analysis confirms statitiscally significant positive associations (r = 0.968 for AI Score vs. Hiring Efficiency; r = 0.946 for AI Score vs. CES; r = 0.924 for AI Score vs. Fairness Index), establishing that audited predictive algorithms optimize talent pipelines while advancing recruitment equity

Mansi Sanjay Dudhakurwe, Prof. Ketaki Kukde, "Artificial Intelligence in Recruitment and Selection: Evaluating Its Impact on Hiring Efficiency, Candidate Experience, and Fairness in Human Resource Management" International Journal of Scientific Research and Engineering Development, V9(5): Page(1897-1900) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.