"The COVID-19 epidemic changed IT industry processes by hastening the introduction of remote work. In virtual workplaces, detecting employee engagement has become essential. Conventional monitoring ignores behavioral measures like mouse activity and screen duration, as well as subtle engagement signs like head movements and facial expressions. Leadership is unable to comprehend employee difficulties and offer prompt assistance because of this detection gap. Resolving this issue will help organizations develop healthier cultures by empowering leaders to address engagement issues, build relationships, and create supportive settings even when people are physically separated. In order to solve this issue, we created a multi-model engagement detection system that uses facial expressions, eye gazing, and head posture as crucial indicators. In order to identify two emotion kinds using face ROIs which are crucial for engagement analysis we developed innovative CNN architectures. Class imbalance in the complicated emotion dataset was handled by a parallel model that concentrated on basic emotions, offering a thorough engagement detection method. The most important facial ROIs indicating engagement were found using XAI algorithms, which also confirmed predictions. Accuracy, precision, recall, and F1-score criteria were used to assess the CNN model in a variety of employee engagement scenarios. The results showed that both basic and complicated emotion prediction models were able to identify patterns of workplace engagement with 61.5% accuracy. Real involvement levels in remote work environments were successfully predicted by combining mouse and screen time monitoring with eye gaze and face angle estimation. "