The integration of digital towers in air traffic control (ATC) intensifies visual complexity of controllers, increasing the risk of detection failure (DF) to warnings and compromising airspace safety. The inherent variability in human situational awareness and behaviors further complicates the differentiation and recognition of various DFs. This study deciphers DF by categorizing it into types based on Endsley’s situation awareness theory, identifying specific causes and key indicators. A four-phase framework—DF classification, DF induction experiment, gaze dynamics analytics, and DF-type recognition—was applied to gaze data from 26 subjects. Results revealed distinct gaze patterns for non-perception, unaware perception, and aware perception of warnings, with continuous warnings weakening operators’ awareness but enhancing foresight of warning implications. A random forest model achieved 80% precision in DF-type recognition, offering empirical support for real-time DF recognition and targeted interventions to improve visual warning detection and human-computer interaction in aviation safety.