Employee turnover remains one of the most significant challenges faced by organizations, as it affects productivity, organizational performance, employee morale, and overall business sustainability. The increasing availability of workforce data and advancements in Human Resource (HR) technologies have enabled organizations to adopt HR analytics as a strategic tool for predicting employee turnover and developing effective retention strategies. HR analytics integrates statistical methods, predictive modelling, machine learning, and data visualization to analyze employee-related information and identify patterns associated with turnover intentions. By examining factors such as employee performance, job satisfaction, compensation, engagement, absenteeism, career progression, and organizational culture, HR analytics helps organizations identify employees who may be at risk of leaving. This predictive capability allows HR professionals to implement timely interventions, including personalized career development, employee engagement initiatives, competitive compensation, training programs, and leadership development. Despite its numerous benefits, the use of HR analytics also raises challenges related to data quality, privacy, ethical concerns, algorithmic bias, and organizational readiness. This study explores the role of HR analytics in predicting employee turnover and enhancing retention strategies. It examines the benefits, limitations, and practical applications of predictive analytics in workforce management while emphasizing the importance of ethical data governance and evidence-based decision-making. The study concludes that organizations adopting HR analytics strategically can improve employee retention, reduce recruitment costs, strengthen workforce planning, and achieve sustainable competitive advantage.