Digital transformation has become a central mandate for modern public administration, particularly in the domain of Human Resource Management (HRM), where data-driven decision-making and automated workflows are redefining efficiency, accountability, and service delivery. This paper examines how business analytics and computational modeling can be integrated into public HRM systems to promote sustainable and resilient local government management. Using a multi-layered analytical design, the study evaluates current digital HRM practices across selected municipal bodies and maps the adoption of HR analytics, machine-assisted forecasting, and rule-based automation. The research incorporates administrative data audits, stakeholder surveys, and computational simulations to assess workforce planning accuracy, service responsiveness, and policy compliance. Results indicate that local governments using predictive analytics and algorithmic decision-support models demonstrated improved resource allocation, reduced procedural delays, and higher transparency in recruitment and performance assessment. However, challenges persist regarding digital skill gaps, data governance maturity, and algorithmic accountability. The findings highlight that sustainable public HRM is achievable when analytics-driven systems are complemented with capacity-building, ethical safeguards, and integrated digital infrastructures. This study provides an evidence-based framework for policymakers and administrators seeking scalable and future-ready HRM reform aligned with long-term governance sustainability goals.