This chapter explores the transformative potential of artificial intelligence (AI) in predicting and proactively managing workplace burnout. By analyzing diverse datasets—from digital communication patterns and workload metrics to physiological signals—AI enables early detection of burnout risk, facilitating timely, personalized interventions. Traditional burnout detection methods, often reactive and limited by self-report biases, are increasingly supplanted by machine learning models such as decision trees, support vector machines, and neural networks that provide dynamic risk assessments. Wearable technologies augment these insights by providing continuous physiological data. AI-driven early warning systems deliver personalized real-time recommendations, promoting healthier work habits and mitigating burnout progression. However, ethical challenges around privacy, consent, and algorithmic bias necessitate transparent governance frameworks. Emerging trends, including augmented intelligence and federated learning, promise to enhance accuracy and privacy.