Various communication technologies are expected to utilize manet. By combining manet with noma communications, one can support scalable, spectrally efficient, and flexible network topologies. To achieve these benefits of noma manet, one should determine the transmission protocol, particularly the superposition code. However, the latter involves lengthy optimization that has to be repeated when the topology changes. In this work, we propose an algorithm for rapidly optimizing superposition codes in multi-hop noma manet. To achieve reliable tunning with few iterations, we adopt the emergingdeep unfoldingmethodology, leveraging data to boost reliable settings. Our superposition coding optimization algorithm utilizes a small number of projected gradient steps while learning its per-user hyperparameters to maximize the minimal rate over past channels in an unsupervised manner. The learned optimizer is designed for both settings with full csi, as well as when the channel coefficients are to be estimated from pilots. We show that the combination of principled optimization and machine learning yields a scalable optimizer, that once trained, can be applied to different topologies. We cope with the non-convex nature of the optimization problem by applying parallel-learned optimization with different starting points as a form of ensemble learning. Our numerical results demonstrate that the proposed method enables the rapid setting of high-rate superposition codes for various channels. Index terms— NOMA, MANET, deep unfolding.