Fast-flying aerial robots promise rapid inspection under limited battery constraints, with direct applications in infrastructure inspection, terrain exploration, and search and rescue. However, high speeds lead to severe motion blur in images and induce significant drift and noise in pose estimates, making dense 3-D reconstruction with neural radiance fields (NeRFs) particularly challenging due to their high sensitivity to such degradations. In this work, we present a unified framework that leverages asynchronous event streams alongside motionblurred frames to reconstruct highfidelity radiance fields from agile drone flights. By embedding event image fusion into NeRF optimization and jointly refining event-based visual inertial odometry priors using both event and frame modalities, our method recovers sharp radiance fields and accurate camera trajectories without groundtruth supervision. We validate our approach on both synthetic data and realworld sequences captured by a fastflying drone. Despite highly dynamic drone flights, where RGB frames are severely degraded by motion blur and pose priors become unreliable, our method reconstructs high-fidelity radiance fields and preserves fine scene details, delivering a performance gain of over 50% on real-world data compared to state-of-the-art methods.