Ground-based remote sensing techniques are crucial in earthquake geology, as they provide high-resolution data for analyzing fault dynamics and earthquake-induced landscape changes. Hyperspectral imagery, in particular, has proven valuable for improving the recording and analysis of paleoseismological trenches. This research aims to enhance workflows that integrate hyperspectral data with high-resolution 3D terrain models. The study focuses on refining earthquake chronologies by improving the mapping and detection of prehistoric earthquake event horizons and structural features in paleoseismological trenches, using the Alhama de Murcia fault in the Eastern Betics Cordillera, Spain as a pilot area—one of the most seismically active regions in the western Mediterranean. Three paleoseismological trenches were excavated and analyzed using logging, georeferencing, and traditional interpretation techniques. Hyperspectral data were captured with a SPECIM Aisa Fenix camera, covering visible to shortwave infrared spectral ranges. High-resolution terrain models with millimeter accuracy were generated using LiDAR and digital photogrammetry. These datasets were processed and corrected using open-source Python tools and integrated into point-cloud data for spectral and dimensional reduction analyses, enabling 3D semiautomatic outcrop mapping. We present the integration of hyperspectral and digital terrain data into point clouds for the three trenches, comparing traditional 2D field logging with 3D digital mapping. At least three paleoearthquake events over the past 34 ka were identified, consistent with previous studies. Additionally, newly identified features reveal further evidence of earthquake imprints. Structural features linked to these events’ surface deformation were detected, including details not visible to the naked eye. Specific image-band ratios also revealed variations in relative mineral abundance along deformation structures, offering new insights into fault damage-zone characterization and fault kinematics. This study highlights the value of hyperspectral data in paleoseismology by reducing uncertainties, validating findings, and revealing previously hidden features through advanced 3D digital mapping techniques.