While the use of Artificial Intelligence (AI) has faced a sharp up-rise in popularity in ground-based industries, such as for autonomous navigation in the automotive industry and predictive maintenance in manufacturing processes, it is yet only rarely used in space industry. Hence, this thesis aims to investigate the possibilities of using AI for processing on-board Earth-orbiting satellites while in orbit. In a first step, the interests and trends of deploying AI on-board satellites are studied, followed by challenges that are hindering the progression of its development. In a second step, five potential on-board applications are selected for investigation of their overall relevance to space industry, as well as their benefits compared to traditional approaches. Out of these, the possibility of using AI for predicting the degradation of batteries is selected for further study, as it shows the highest potential. Today’s approaches for monitoring battery degradation on satellites are highly insufficient and there is a great demand for a new approach. Several AI-based methods have been proposed in literature, but only rarely for processing directly on-board. Thus, I investigate the feasibility of adopting such an algorithm for on-board use, including an evaluation of the suitability of different algorithms, as well as the choice of input parameters and training data. I find that the use of AI could highly improve various aspects of satellite performance both on a platform and a payload level, by making them more efficient, but also more capable, such as for in-orbit battery prediction on-board. However, its implementation is still heavily hampered by the lack of validation and verification standards for AI in space, along with limitations imposed by the space environment, restricting the satellite design. In the investigation of using AI for on-board battery prediction, I find that this would be a suitable application for constellation satellites in LEO, in particular for prolonging their operations beyond their planned lifetime while still being able to ensure safe decommissioning. I estimate that this would lead to a yearly minimal average saved satellite replacement cost of $ 22 million in a constellation with 500 satellites, assuming an extension of the satellite lifetime from 7 to 7.5 years when using this application. Based on references in literature, I find that using a Long Short-Term Memory (LSTM) algorithm could make the most intricate predictions, whereas a Gated Recurrent Unit (GRU) algorithm would be less processing-heavy at the cost of a loss in accuracy. Training needs to be done on ground, either on telemetry data from past, similar missions or on synthetic data from simulations. Its implementation needs to be investigated in future research, including the selection of a suitable framework, but also benchmarking for evaluating the necessary processing power and memory space.