A mixed-methods design was used with a sample of 225 employees working for medium-to-large-sized organizations selected through stratified random sampling to cater for departmental, job-related, and demographic diversity. There was a 6-month operational pilot of the system, and it was subjected to statistical analysis using pre- and post-intervention data. Results revealed significant decreases in emotional exhaustion (–25.8%) and depersonalization (–31.1%) and a substantial increase in personal accomplishment (+22.2%). The Deep Neural Network was the best-performing model compared to the other models (Accuracy = 91%, ROC-AUC = 0.94), demonstrating the strength of the developed burnout detection framework. The effectiveness of the intervention differed by risk group, with employees at high risk for burnout showing the highest reduction in burnout level (42.1%), followed by moderate-risk (28.7%) and low-risk (12.4%) groups, which demonstrates that an intervention can be more effective when targeted towards individual profiles. [16] Our results show that AI-based mental health support systems can improve early detection and individualized management of workplace burnout, promote better employee health, and contribute to organizational health initiatives. The researchers stress the need to combine ethical AI with the latest governance and privacy solutions and human-center control and oversight to ensure trust and confidence in responsible use. Further studies are warranted using a longitudinal design to examine the long-term effect of such interventions and possible cross-sector adaptation. By combining innovative technology with psychological support, this study adds to rapidly emerging literature about the role of AI in workplace mental health solutions.