Human Resources Analytics for Predicting Employee Turnover is an emerging area that applies data-driven techniques to identify, analyse, and forecast the likelihood of employees leaving an organization. In today’s competitive business environment, high employee turnover can lead to increased recruitment costs, loss of skilled talent, reduced productivity, and decreased organizational performance. This study focuses on the use of HR analytics tools and predictive models to examine key factors influencing employee attrition, such as job satisfaction, compensation, work environment, performance ratings, career growth opportunities, and employee engagement. By collecting and analysing historical employee data, statistical methods and machine learning techniques are used to identify patterns and trends that signal potential turnover risks. The research aims to develop a predictive framework that enables organizations to take proactive retention strategies, improve workforce planning, and enhance decision-making processes. Ultimately, the study highlights how integrating analytics into HR functions can transform traditional human resource management into a strategic, evidence-based approach that supports long-term organizational sustainability and employee satisfaction.