Human Resource Management (HRM) has undergone a significant transformation with the integration of Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics into organizational decision-making processes. Traditional human resource practices, which primarily relied on managerial experience and historical performance records, are increasingly being replaced by intelligent HR analytics capable of extracting actionable insights from large and complex workforce datasets. AI-driven HR analytics enables organizations to optimize recruitment, employee performance evaluation, talent acquisition, workforce planning, retention strategies, succession planning, learning and development, and employee engagement through predictive and prescriptive analytical models. Machine learning algorithms facilitate accurate prediction of employee turnover, identification of high-potential employees, personalized career development recommendations, and automated recruitment screening while reducing operational costs and improving decision quality. Despite these advantages, organizations continue to face challenges associated with algorithmic bias, data privacy, ethical AI implementation, transparency, and workforce acceptance. This study investigates the integration of AI-driven HR analytics with strategic human resource decision-making. It examines machine learning applications, analytical frameworks, implementation challenges, ethical considerations, and organizational benefits associated with intelligent talent management systems. The findings suggest that AI-enabled HR analytics significantly improves strategic workforce planning, enhances organizational performance, and supports evidence-based decision-making while emphasizing the need for responsible AI governance and human-centered implementation strategies..