<p>Glacial Lake Outburst Floods (GLOFs) are emerging as a critical global hazard in high mountain regions, driven by accelerated glacial retreat and the destabilization of dammed lakes under a changing climate. In the Himalayas, GLOFs pose growing threats to downstream communities, infrastructure, and ecosystems. The Eastern Himalayas, characterized by steep topography, active tectonics, high seismicity, and frequent landslides, represent a particularly high-risk zone. However, most GLOF risk assessments to date have focused primarily on glaciological and hydrological parameters, often neglecting the compounded effects of seismic and landslide hazards. This study fills this vital gap by creating an integrated, multi-hazard GLOF risk assessment framework designed specifically for the Eastern Himalayas. Thirteen geospatial parameters—such as glacial lake morphology, proximity to faults, seismic exposure, landslide frequency, historical GLOF events, land cover, and urban proximity—are assessed via a Multi-Criteria Decision Analysis (MCDA) approach. We employ a combination of Analytic Hierarchy Process (AHP) and Shannon Entropy for parameter weighting, while Kullback–Leibler divergence is used to enhance analytical discrimination. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) generates lake-specific risk indices, which are subsequently classified using Fuzzy C-Means (FCM) clustering. The analysis reveals that out of 1,144 glacial lakes, 432, 368, 227, and 117 are categorized as low, moderate, high, and very high-risk, respectively. Importantly, 95.1% of lake classifications were assigned to a confidence level exceeding 80%. These findings underscore the value of integrating geomorphic, seismic, and mass-wasting processes in GLOF risk modelling, offering a transferable methodology for hazard assessment in other tectonically active mountain systems worldwide.</p>