Braided rivers are among the most dynamic natural Earth systems, with a rapid and complex morphological evolution. Limited understanding and inadequate algorithm implementation of specific processes affect the accuracy of physics-based models. This leads to uncertainties that complicate the effective design of interventions and protection measures. In recent years, artificial intelligence techniques rapidly advanced and the availability of satellite imagery products increased. This research sets a novel attempt to predict the planform evolution of braided rivers with deep learning using satellite images. The study focuses on the middle and lower reaches of the Brahmaputra-Jamuna River in India and Bangladesh. We developed JamUNet, a U-Net-based convolutional neural network (CNN). The model is trained with the Global Surface Water Dataset (GSWD) to classify each pixel as "Non-water" or "Water". Four images from the same month over four consecutive years were used as input. The fifth year image served as target. JamUNet demonstrates a general capability in capturing the planform evolution. Morphological processes like meander migration, channel abandonment, and confluence and bifurcation development are often well captured. However, temporal patterns are lacking. More complex phenomena, like channel formation and channel shifting, remain unpredicted. JamUNet also tends to underpredict the total areas of erosion and deposition. Overall, JamUNet achieves a 5-6% improvement compared to the benchmark method for which no morphological change occurs in metrics such as precision, recall, F1-score, and critical success index (CSI). Among these, recall is the most meaningful metric for evaluating the model performance. JamUNet can serve as a preliminary tool for water management authorities in India and Bangladesh. It can assist in prioritising bank protection in erosion-prone areas and support land reclamation projects and inland navigation. Caution is always advised due to the model tendency to underpredict erosion. More research is required to improve the current model. Nonetheless, deep-learning modelling seems a promising field of research. Testing alternative model architectures and incorporating additional data, such as water levels or river discharge, could improve the model performance.