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There exists a narrow engineering corridor across the Qinghai-Tibet Plateau (QTP) with widely distributed slopes, called the Qinghai-Tibet Engineering Corridor (QTEC), where a variety of important infrastructure is concentrated. These facilities are the transportation routes for people, materials, energy, etc. from mainland China to Tibet. From Golmud to Lhasa, the engineering corridor covers 632 kilometers of permafrost containing the densely occurring Qinghai-Tibet Railway, Highway, and power/communication towers. Slope failure in permafrost regions, caused by permafrost degradation, ground ice melting, etc., and affects the engineering construction and permafrost environments in the QTEC. We implement a variety of sensors to monitor the hydrological-thermal deformation between the permafrost slopes and permafrost engineering projects in the corridor, and the aforementioned sensors are densely located on or around the permafrost slopes. In addition to soil temperature and moisture sensors, the global navigation satellite system (GNSS), terrestrial laser scanning (TLS), and unmanned aerial vehicles (UAVs) were adopted to monitor the thermal deformation spatial distribution and changes. An integrated dataset of hydrological-thermal deformation in permafrost engineering and slopes in the QTEC between 2014 and 2019, including meteorological and ground observations, TLS point cloud data, and RGB and thermal infrared (TIR) images, can be of great value for estimating the hydrological-thermal impact and the stability between engineering and slopes under the influence of climate change and engineering disturbance. The dataset and code were uploaded to the Zenodo repository and can be accessed through https://zenodo.org/communities/qtec, including Meteorological and ground observations at http://doi.org/10.5281/zenodo.3764273 (Luo et al., 2020e), TLS measurements at http://doi.org/10.5281/zenodo.3764502 (Luo et al., 2020b), UAV RGB and TIR images at http://doi.org/10.5281/zenodo.3764280 (Luo et al., 2020c), and R code for permafrost indices and visualization at http://doi.org/10.5281/zenodo.3766712 (Luo et al., 2020d). [ABSTRACT FROM AUTHOR]