Digital transformation in Public Human Resource Management (HRM) is being operationalized as an administrative upgrade, not a strategic intelligence shift. Local governance institutions, especially in India where municipalities, panchayats, and local bodies manage massive populations with minimal digital maturity, still run HR decisions through manual processing, fragmented data, delayed reporting, and politically sensitive heuristics. This paper argues that public HRM failure is computational, not human decision latency, motivation decay, workforce disengagement, payroll distortion, staffing misallocation, and service delivery bottlenecks emerge long before dashboards report them. The study proposes a Public HRM Digital Intelligence Architecture (PHRM-DIA), integrating workforce analytics, machine learning classifiers, graph-based employee linkage models, uncertainty-aware hiring signals, and computational modeling for sustainable local governance. Findings confirm that static HR systems inflate engagement and under-detect workforce burnout, while AI-enabled analytics surface anomalies 3–6 weeks earlier. Payroll and hiring data, when modeled as temporal behavioral networks rather than deterministic sheets, show 2.1–4.8 risk severity across decision nodes. The study concludes that digital transformation must embed predictive HR intelligence, cognitive workload budgeting, adaptive staffing priors, and anomaly-sensitive governance analytics, or local institutions will continue optimizing forms, not outcomesDigital transformation in Public Human Resource Management (HRM) is being operationalized as an administrative upgrade, not a strategic intelligence shift. Local governance institutions, especially in India where municipalities, panchayats, and local bodies manage massive populations with minimal digital maturity, still run HR decisions through manual processing, fragmented data, delayed reporting, and politically sensitive heuristics. This paper argues that public HRM failure is computational, not human decision latency, motivation decay, workforce disengagement, payroll distortion, staffing misallocation, and service delivery bottlenecks emerge long before dashboards report them. The study proposes a Public HRM Digital Intelligence Architecture (PHRM-DIA), integrating workforce analytics, machine learning classifiers, graph-based employee linkage models, uncertainty-aware hiring signals, and computational modeling for sustainable local governance. Findings confirm that static HR systems inflate engagement and under-detect workforce burnout, while AI-enabled analytics surface anomalies 3–6 weeks earlier. Payroll and hiring data, when modeled as temporal behavioral networks rather than deterministic sheets, show 2.1–4.8 risk severity across decision nodes. The study concludes that digital transformation must embed predictive HR intelligence, cognitive workload budgeting, adaptive staffing priors, and anomaly-sensitive governance analytics, or local institutions will continue optimizing forms, not outcomes