Human Resource Management (HRM) in Nigerian universities is often constrained by nepotism, workload imbalance, and biased promotion practices, which undermine staff morale and institutional effectiveness. This study introduces an Artificial Intelligence (AI)-driven Human Resource Management System (HRMS) that integrates Machine Learning (ML) models to address these systemic challenges. The framework leverages structured staff data—covering teaching load, research output, administrative responsibilities, and professional development activities—to generate objective, data-driven insights for performance evaluation, workload distribution, and promotion recommendations. By embedding predictive analytics within a scalable, web-based system, the HRMS enhances decision-making transparency and reduces reliance on subjective judgment. Evaluation results indicate significant improvements in fairness of performance reviews, equitable distribution of teaching and administrative duties, and lecturer satisfaction with promotion processes. To the best of our knowledge, this work represents the first AIdriven HRM framework tailored to the Nigerian higher education context, providing a practical pathway for reducing favoritism and fostering accountability in academic administration. The study contributes to the growing body of knowledge on technologyenabled HRM in universities and offers policy-relevant insights for advancing equity and efficiency in higher education governance