<![CDATA[In the era of Industrial Revolution 4.0, the integration of artificial intelligence (AI)-based analytics in human resource management (HRM) has become important to improve employee welfare and productivity. However, challenges related to algorithm bias and transparency in the use of AI are still major concerns. This research aims to evaluate the effectiveness of AI-based HR analytics in predicting employee well-being and productivity in various organizations. Using a Systematic Literature Review (SLR) approach, this research analyzes 69 relevant peer-reviewed studies, with a focus on data collection techniques and analysis methods applied, including thematic analysis and framework analysis. QFindings show that the use of AI-based predictive models, such as Natural Language Processing and Deep Learning, can increase the accuracy of employee well-being predictions by up to 92%. Additionally, holistic data integration strengthens understanding of employee dynamics and improves strategic decisions in HRM. This research makes a significant contribution to the development of HRM theory and practice by emphasizing the importance of transparency and ethics in the application of AI. It is hoped that these findings will encourage organizations to adopt more effective and sustainable data-driven approaches.]]>
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