Unmanned Aerial Vehicles (UAVs) require accurate and robust motion estimation to enable autonomous navigation in complex environments. Event cameras sensor, a bio-inspired sensors capturing high-temporal-resolution brightness changes, provide an attractive vision modality under challenging conditions such as high dynamic range and rapid motion. However, event-based sensor optical flow estimation remains challenging due to noise, sparsity, and occlusions inherent in event data, limiting UAV performance. In this work, we present a novel event-based sensor optical flow estimation framework that integrates a lightweight OcclusionNet to pre-emptively mask occluded and noisy events, enhancing the downstream Contrast Maximization (CM) optimization. Our approach extends classical CM by incorporating occlusion-aware data filtering, reducing overfitting, improving convergence, and preserving the sharpness of the image of warped events (IWE). Quantitative evaluation demonstrates our method achieves competitive end-point errors (EPE ≈ 1.7 pixels) and flow angular errors, closely matching ground-truth flow warping loss metrics, while eliminating large error outliers. The model was evelauted on a 1th Gen Intel Core i7-13700 with 32GB RAM, achieving approximately 0.09 seconds per frame on the MVSEC indoor_flying sequences. Qualitatively, our occlusion-aware flow yields sharp IWEs and robust performance under complex motions and lighting, showing strong potential for UAV tasks. This work highlights the benefits of integrating occlusion reasoning into event-based flow estimation, paving the way for reliable and efficient sensory for UAV perception in industry 4.0 inspection system.