High turnover rates in the public health workforce pose ongoing challenges to maintain essential services and institutional knowledge. Recent studies indicate that job dissatisfaction, burnout, and structural barriers have intensified following the COVID-19 pandemic. While prior studies have identified key predictors of turnover intention, the potential of machine learning to improve predictive accuracy and guide targeted interventions remains underexplored.

This article explores how various machine learning techniques can help improve turnover predictability and simulate the impact of workplace satisfaction improvements among state health agency employees.