Landing a spacecraft is a challenging task as there are different external (environment)and internal (measurements) factors that plays essential role to precisely land the ship in the target with small error margins. Even though the task is difficult, the success of such project would result in cost reduction through energy and resource efficiency. The task is non trivial as the dynamics of the spacecraft is non-linear and uncertain, which leads to difficulty in constructing models. Different Machine Learning techniques could be used to build such models, however this results in a non linear complex representation which is difficult to understand. The process and results of are not quite interpretable, and act as a”black box” mapping of input and output. Thus, the main idea of this thesis is to use the Koopman operator to model the nonlinear dynamics of the spacecraft into a higher dimensional lifted feature space where its evolution is linearly represented. The linear operators built on the Koopman operator framework is simple, data driven and used to design controllers, in our case linear Model Predictive Control without any non linear optimization schemes. Furthermore, we explore how the predictors obtained through this method is comparatively superior in terms of performance to the existing linear predictors. The model is efficient when used in Model Predictive Control scheme so that control loop optimization can be done really fast. Thus, due to efficient linear model controlling techniques, it is good for the real time systems.