The Adjusters International disaster response report indicated that fatigue-related accidents are common in many industries; thus, they relate to the emergency response and require reliable monitoring solutions. In the proposed system, facial features related to the eye are going to be continuously tracked using the Dlib library: head position, closure, and mouth movements. Therefore, the proposed system would sound a local alarm after 10 s before detecting the eyes closed. When a repeat event is detected, it can also send notification escalations to family members via the Twilio API. It looks out for prolonged signals of sleepiness, and any unusual head movement, for a complete fatigue assessment. The system is powered by real-time processing that allows it to take immediate action when fatigue has been detected, and location data integrations. Enhances response to intervention. The system produces detailed session reports that facilitate long-term analysis of fatigue patterns. The results appear to be very high in recognizing various degrees of fatigue.