Organizations face persistent challenges in employee retention, with burnout and disengagement being primary drivers of attrition. Traditional retention strategies often rely on reactive approaches, failing to identify early warning signs. This study proposes an AI-driven sentiment analysis framework that integrates Natural Language Processing (NLP) and Multiple Criteria Decision Making (MCDM) to detect early indicators of disengagement and burnout in workplace communication. By analyzing emails, chat logs, HR feedback, and survey responses, the model identifies sentiment trends, emotional tone, and linguistic patterns linked to declining employee engagement. The extracted sentiment scores are incorporated into an MCDM-based decision model, ranking key risk factors such as leadership influence, job satisfaction, and workload. This enables HR teams to implement personalized interventions for at-risk employees. The proposed methodology is validated using real-world organizational datasets, focusing on the accuracy of early detection and intervention effectiveness. Findings indicate that AI-powered sentiment insights, when combined with structured decision models, significantly enhance retention strategies by proactively addressing workplace stressors. This research contributes to AI-driven HR analytics, offering a scalable solution for improving employee engagement and reducing turnover.