Moraine-dammed glacial lakes in the Himalayas are rapidly expanding due to glacier retreat, significantly increasing the risk of glacial lake outburst floods (GLOFs) to downstream settlements and infrastructure. In this study, we present a comprehensive assessment of GLOF risk in the Poiqu-Bhotekoshi and Gyirong-Trishuli transboundary basins, which span between China and Nepal. We employed Machine Learning (ML) models, initially trained in the Himalayas, to evaluate GLOF susceptibility in these basins. Also, we conducted hydrodynamic modeling of two representative glacial lakes for multi-dam break scenarios (high, medium, and low). These outcomes were intersected with publicly available physical infrastructure data to assess exposure and risk. Our findings identify 28 glacial lakes (>= 0.01 km2) as highly susceptible to GLOFs. In extreme scenarios, modeled lakes could release peak discharges ranging from 7532 to 38,220 m3/s and inundate up to 60 m depth, surpassing seasonal high-flow floods by up to ten times. Our analysis indicates that over 3000 buildings, about 50 bridges, nine hydropower sites, and around 50 km of the road could be at risk from anticipated GLOFs. Specifically, six sub-districts in Nepal and two counties in China have been identified as high risk. This study is expected to help authorities and policymakers in both countries in developing joint-ventured holistic disaster risk reduction strategies, thereby mitigating the GLOF risk in these vulnerable transboundary basins.