This paper reviews the current status and research progress of machine learning (ML) applications in human resource management (HRM). It specifically focuses on algorithmic applications in employee turnover prediction, recruitment and talent screening, and performance evaluation. The paper highlights ML's significant role in enhancing HRM efficiency and accuracy through innovations in algorithms and data processing. It also identifies challenges such as the complexity of HR phenomena, limitations of small datasets, ethical issues (including fairness and legal compliance), and employee reactions to algorithmic decisions. Finally, it outlines future directions, emphasizing algorithmic transparency, cross-domain data fusion, and in-depth ethical and legal research to promote innovative ML applications in HRM.

Tianyu Zhao. Artificial Intelligence in Human Resource Management: Frontier Advances and Challenges of Machine Learning Algorithms. Academic Journal of Business & Management (2025), Vol. 7, Issue 12: 82-86. https://doi.org/10.25236/AJBM.2025.071211.

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