In recent years the need of an augmented spacecraft autonomy has considerably increased along with the renewed interest in Lunar missions. Low-Thrust Cislunar trajectory optimization is typically achieved leveraging classical optimal control methods. These methods can be time expensive, challenging for mission analysts and they often require nontrivial computational effort, due to the non-linear nature of the three body dynamics problem. The aim of the thesis is to develop a neural network to re-compute the optimal control history of a transfer maneuver autonomously on board, in shorter time and with low computational effort. The main core of the work logic is the collection of different datasets of optimal transfer maneuvers between typical lunar orbits.