Human Resource (HR) analytics has become an essential component of modern Human Resource Planning, especially with the integration of machine learning (ML). HR analytics refers to the systematic collection, measurement, analysis, and interpretation of employee-related data to improve organizational decision-making. Machine learning enhances HR analytics by enabling computers to identify patterns, learn from historical data, and make predictions without being explicitly programmed for every task. Together, HR analytics and machine learning help organizations plan, manage, and develop their workforce more effectively. In Human Resource Planning, organizations need accurate information regarding workforce demand, employee performance, talent acquisition, retention, training needs, and succession planning. Traditional HR practices relied heavily on manual observations and experience, whereas machine learning-based HR analytics provides data-driven insights that improve the quality of HR decisions. One of the major significances of HR analytics in machine learning is predictive workforce planning. Machine learning algorithms analyze historical workforce data, business growth trends, employee turnover, recruitment patterns, and market conditions to predict future staffing requirements. This enables HR managers to estimate how many employees will be required, what skills they should possess, and when recruitment should begin. Such predictive planning reduces both employee shortages and workforce surplus. Another important application is employee recruitment and selection. Machine learning models can analyze thousands of job applications within a short period by comparing candidates' qualifications, skills, experience, certifications, and previous performance indicators. HR analytics identifies the characteristics of successful employees and helps recruiters shortlist candidates who closely match these profiles. This reduces recruitment time, improves hiring quality, and minimizes human bias in the initial screening process. For example, a multinational IT company receives over 20,000 applications for software developer positions. A machine learning model analyzes previous hiring data and identifies candidates with skills, educational backgrounds, certifications, and experiences similar to high-performing employees already working in the organization. The recruitment team can then focus only on the