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River plumes play an important role in the formation of coastal marine ecosystems; however, their automatic detection from satellite data is complicated by blurred boundaries, high shape variability, and weak contrasts with the surrounding water area. This paper considers the application of deep learning methods for the segmentation of river plumes in satellite images. Optical data from MSI sensors of Sentinel-2 satellites, as well as OLI/OLI-2 of Landsat-8/9 satellites with spatial resolution of 10–30 m were used. The study was performed for the areas of Sulak, Terek, Ural, and Kura rivers in the Caspian Sea, the Mzymta River in the Black Sea, and the Kaliningrad Bay outflow and the Vistula River in the Baltic Sea. The first contribution of this work is a dedicated expert-annotated dataset of 744 satellite images covering seven estuarine systems in three seas, combined with an adaptive, brightness-aware photometric augmentation pipeline; on this basis, convolutional and transformer segmentation architectures were compared, including U-Net, DeepLabv3+, FCN, YOLO11seg, SegFormer, and Mask2Former. Quality assessment was conducted using the Dice coefficient, Precision/Recall metrics, and Hausdorff distances. Second, we propose a weighted ensemble segmentation framework in which the predictions of four complementary architectures (U-Net, DeepLabv3+, YOLO11seg, and Mask2Former) are combined with weights proportional to their validation performance; the resulting ensemble outperforms every individual model in overall mean per-scene Dice on the test set, and its lowest regional score remains above the lowest regional score of any individual model.