Burnout Prediction and Workforce Analytics Using Scientifically Validated Behavioral Models

Burnout has turned into one of the most pressing and measurable problems in the contemporary management of the workforce, specifically in those areas that are most exposed to emotional work-related stress and performance pressure. This review includes the use of scientifically proven behavioral models to predict and prevent burnout with sophisticated workforce analytics. Using the latest interdisciplinary literature, the paper has examined how behavioral science, artificial intelligence, data analytics, machine learning, and federated learning models could be combined to identify early signs of emotional exhaustion, workplace deviance, and disengagement. It identifies leadership styles, organizational culture, employee proficiency, and engagement measures as some of the factors that affect psychological well-being. In addition, the review explains how the job demands-resources theory and established clinical tools, including nomograms, can be used in stress management strategies. With the synthesis of evidence in different organizational and technological contexts, the paper provides a holistic evaluation of how predictive models are changing employee wellness and retention policies in modern organizations.