In today’s era of remote work and virtual collaboration, online meetings have become a crucial part of everyday communication. However, keeping participants genuinely engaged and focused in these digital settings remains a significant challenge. The success of virtual meetings often depends on how well participants stay attentive, and traditional ways of gauging engagement—like relying on verbal cues or feedback—aren’t always effective in this digital world. This project introduces an innovative approach using computer vision technologies for real-time attention monitoring. This method provides a more intuitive and accurate way of understanding participant focus, without requiring explicit feedback. Beyond the familiar advantages of accessibility and affordability, online platforms have revolutionized global connections, making it simpler to conduct interviews, host seminars, and collaborate across distances seamlessly. To meet the demand for reliable attention detection, a system was developed using a powerful boosting algorithm. Specifically, the approach leverages AdaBoost, a robust learning technique that builds a strong classifier by selecting key visual features from simpler models and combining them effectively. This ensures that the system can quickly and accurately process facial images, achieving high detection rates and setting the stage for more engaging and productive online experiences.