Author: Diana Ussher-Eke, Abba Benedict Ojoago, Onuh Matthew Ijiga, Joy Onma Enyejo

Doi: 10.26480/mjhrm.02.2025.98.107

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

As organizations increasingly rely on data-driven strategies, Human Resources (HR) departments are exploring advanced technologies to enhance talent acquisition and retention. This review paper examines the integration of predictive analytics into HR planning, with a particular focus on the application of deep learning models. By leveraging large volumes of workforce and behavioral data, deep learning enables more accurate predictions of employee performance, turnover risk, and recruitment outcomes. The paper explores the evolution of predictive analytics in HR, current deep learning techniques in use, and their practical implications. Additionally, it discusses challenges such as data privacy, bias, and model interpretability, and provides a future outlook on AI-driven HR practices. The goal is to provide a comprehensive understanding of how deep learning can transform HR functions into more strategic, efficient, and employee-centric processes.