Glacial lakes in the southeastern Tibetan Plateau (SETP) have expanded, increasing the potential for cascading hazards associated with glacial lake outburst floods (GLOFs). However, long-term, annual monitoring data that include micro glacial lakes remain relatively limited for this region. To address this gap, this study integrated Landsat series and Sentinel-2 imagery and used the GLA-RCNN deep learning framework with an embedded Convolutional Block Attention Module to construct and release an annual glacial lake inventory (SETP_GLI). The dataset comprises 36 annual vector layers from 1990 to 2025, recording the annual evolution of regional glacial lake numbers and areas. The use of 10 m resolution imagery and model optimization improved the detection of micro glacial lakes (<0.01 km²). The inventory provides annual vector boundaries and standardized physical attributes—including longitude, latitude, area, perimeter, and mean elevation, together with area uncertainty metrics derived from mixed-pixel theory. Quality assessments indicated that the extraction framework is robust against interference from mountain shadows and turbid water. For model performance, the overall F1 scores for typical years remained above 0.82 (with a maximum of 0.895); cross-validation with existing public databases (Hi-MAG and Glacial lake inventory of high-mountain Asia) showed that the matched polygon-level Intersection over Union (IoU) ranged from 0.54 to 0.80, with spatial agreement increasing with improvements in historical image quality. Spatiotemporal analysis revealed a persistent expansion trend, with the annual area growth rate rising from 3.65 ± 1.12 km² a⁻¹ (1990–2012) to 5.95 ± 2.44 km² a⁻¹ (2016–2025). The dataset is archived at the National Tibetan Plateau Data Center (TPDC) (<a href="https://doi.org/10.11888/Cryos.tpdc.303491" target="_blank" rel="noopener">https://doi.org/10.11888/Cryos.tpdc.303491</a>), with processing code released openly. SETP_GLI serves as a baseline dataset for cryospheric response analysis, hydrological modeling, and GLOF risk assessment.