Prolonged interaction with digital displays is a leading cause of Digital Eye Strain (DES), yet continuous, affordable, and non-intrusive ocular fatigue assessment remains largely unavailable outside clinical settings. This paper presents SentryEye, a real-time eye strain and fatigue monitoring framework that integrates MediaPipe facial landmark extraction, adaptive Eye Aspect Ratio (EAR) analysis, PERCLOS quantification, deep learning-based strain classification, gaze estimation, and HSVbased eye redness scoring within a unified Flask–Socket.IO web architecture. Webcam frames are processed at 25–30 FPS entirely on standard CPU hardware; a 60-second adaptive calibration phase personalizes blink-detection thresholds, and a seven-condition alert engine with per-condition cooldown intervals delivers synchronized audio-visual notifications. Experimental validation on a three-class eye image dataset (Normal, Mild Strain, High Strain) partitioned in an 80:20 training-tovalidation ratio shows that the optimized MobileNetV2 transfer-learning classifier attains 92.3% validation accuracy, a weighted F1-score of 91.9%, a ROC-AUC of 0.971, and approximately 12 ms per-frame CPU inference, outperforming a custom EyeNet CNN baseline. The end-to-end pipeline sustains sub-50 ms Socket.IO metric emission latency and an average alert detection accuracy of 95.9% across all seven monitored conditions, confirming the deployability of the framework for institutional, workplace, and remote digital-wellness monitoring. Novelty in the Research SentryEye advances prior art on four fronts. First, it introduces an integrated, modular five-layer lightweight pipeline that executes facial mesh tracking, EAR/PERCLOS computation, CNN classification, temporal prediction, and telemetry emission concurrently at 25–30 FPS on a standalone CPU, without GPU acceleration or cloud offloading. Second, it embeds an adaptive personalized 60- second baseline EAR calibration mechanism that derives user-specific closure thresholds from the top-90% sorted sample distribution, reducing false blink,that manages seven synchronous physiological and behavioural factors—multi-face presence, prolonged eye closure, sustained fatigue, low blink rate, high PERCLOS, 20-20-20 screen-time compliance, and HSV-based eye redness—coupled with programmatic native sound synthesis through the Web Audio API, eliminating external audio dependencies. Fourth, it provides real-time interactive local reporting provides real-time interactive local reporting via a Flask–Socket.IO–Chart.js dashboard with sub-50 ms emission latency, delivering complete analytics ondevice without any cloud dependency.