Accurately predicting human egocentric gaze events, such as saccades, fixations, and blinks, holds transformative potential for virtual reality (VR) applications. By using eye-trackers integrated into wearable head-mounted displays, it is possible to optimize runtime, improve user experience, and integrate gaze into downstream tasks. However, predicting gaze events remains challenging as the temporal dynamics of egocentric gaze events are multifaceted, for example, influenced by visual stimuli and task demands. While deep learning and machine learning offer promising avenues, the investigation of these approaches in gaze event prediction remains largely uncharted. In this talk, we present recent advances in recurrent time-to-event analysis for gaze event prediction, addressing the pressing challenges of temporal modeling and real-time prediction. This talk will discuss the potential implications, challenges, and techniques of accurate gaze event prediction in the context of egocentric vision with a focus on VR.