Neural stimulation can alleviate or even reverse paralysis and sensory deficits. Rapid technological advancements bring the possibility to develop complex and refined patterns of neurostimulation. However, multipronged interventions with high-density neural interfaces will require algorithmic frameworks to handle optimization in large parameter spaces. Here, we used an algorithmic class, Gaussian-Process (GP)-based Bayesian Optimization (BO), to solve this online problem. We show that GP-BO can efficiently explore the neurostimulation parameters’ space, exceeding extensive search performance after testing only a fraction of the possible combinations. It can quickly optimize multi-channel neurostimulation across diverse biological targets (brain and spinal cord), animal models (rats and non-human primates), in healthy and injured subjects. Moreover, since BO can embed and improve ‘prior’ expert/clinical knowledge, the performance can be dramatically enhanced even further. These results support broad establishment of learning agents as a structural part of neuroprosthetic design, enabling therapeutic personalization and maximization of intervention effectiveness.