Radiologist burnout has become a significant global concern due to the rapid increase in imaging volumes, administrative responsibilities, and continuous diagnostic workload. Studies report that a large proportion of radiologists experience symptoms such as emotional exhaustion, depersonalization, and reduced professional satisfaction. Increasing case complexity, time pressure, and repetitive cognitive tasks contribute to mental fatigue and reduced diagnostic performance, making burnout a critical workforce and patient-safety issue. Artificial intelligence [AI] tools are increasingly being integrated into radiology workflows and can prioritize imaging worklists, generate draft reports, and analyze operational data to detect patterns associated with workload stress and cognitive fatigue. Evidence suggests that AI-assisted reporting systems can reduce interpretation time and improve workflow efficiency without compromising diagnostic accuracy. Additionally, predictive models using electronic health record activity logs have been proposed to detect early signals of clinician burnout through behavioral analytics and digital workflow metrics. This review aims to explore the emerging role of artificial intelligence in predicting radiologist burnout and cognitive fatigue through workflow analytics. It seeks to synthesize current literature on AI-based monitoring of radiologist workload, predictive modeling of burnout risk, and the potential of intelligent systems to support sustainable radiology practice and improve diagnostic performance.