<p>Environmental impact assessments (EIAs) for railway projects in Korea remain largely qualitative and rely on standardized guidelines, which limit their capacity to reflect site-specific and temporal environmental variations. This study introduces a digitalized framework for assessing air quality and noise in railway Environmental impact assessments (EIAs) through the integration of drone and LiDAR technologies. The Osong railway test track was selected as a demonstration site, where time-series measurements of particulate matter and noise were conducted under varying altitudes, distances, and structural configurations. A high-resolution digital twin of the site was developed using photogrammetry and three-dimensional (3D) point cloud data. The analysis revealed significant correlations between Particle matter (PM) concentrations and microclimatic factors such as humidity and wind speed, and the drone-based noise data exhibited strong consistency with conventional ground measurements. These results confirm that the proposed drone–LiDAR approach enhances the spatiotemporal accuracy and reproducibility of environmental observations. The study concludes that this framework can substantially improve the objectivity and scalability of railway Environmental impact assessments (EIAs) and can be extended to other linear infrastructure projects to support sustainable environmental management.</p>