Human resource (HR) optimization has grown to be a top priority for companies trying to raise performance and efficiency. Companies today embrace data-driven strategies to improve HR operations including workforce planning, employee engagement, and recruitment as analytics have grown in prominence. With an eye on its advantages and drawbacks, this study reviews the literature to investigate how analytics is used in HR improvement. To examine already published studies, the researcher makes use of databases, academic publications, and Google Scholar among other sources. Results imply that predictive modeling and machine learning among analytics tools enable companies to make better HR decisions, raise employee happiness, and forecast future labor requirements. Still, difficulties including data protection and the demand for qualified analysts still exist. The study comes to the conclusion that even while analytics presents great possibilities for HR optimization, more study is required to completely grasp its long-term influence and efficiency.