<![CDATA[Managing human resources (HR) using data and technology has become a strategic necessity in dealing with increasingly complex business environments. The Evidence-Based Human Resource Management (EBHRM) approach offers a framework that emphasizes decision-making in HR based on scientific evidence, empirical data, and predictive analytics. This study aims to comprehensively examine the role of digital HRM, HR analytics, machine learning, and High-Performance Work Systems (HPWS) in enhancing the quality of strategic decision-making within organizations. The research used a Systematic Literature Review (SLR) approach following the PRISMA guidelines, including 14 articles indexed in Scopus (Q1–Q4) published between 2020 and 2025. The findings indicate that HR analytics improves the effectiveness of HPWS, digital HRM creates a competitive advantage, machine learning is capable of predicting employee performance risks and turnover, and optimization algorithms such as the Modified Single Candidate Optimizer (MSCO) enhance the efficiency of human resource allocation. The study concludes that integrating data and technology into HR functions strengthens EBHRM, leading to more accurate, objective, and strategic decisions. These findings provide managerial implications for organizations to strengthen their digital HRM infrastructure, enhance analytical capabilities, and implement evidence-based HR policies to improve competitiveness and organizational sustainability.]]>
