<p>Our modern society is more dependent on its assets in space now more than ever. For<br> example, the Global Positioning System (GPS) many rely on for navigation uses data from a<br> 24-satellite constellation. Additionally, our current infrastructure for gas pumps, cell phones,<br> ATMs, traffic lights, weather data, etc. all depend on satellite data from various constel-<br> lations. As a result, it is increasingly necessary to accurately track and predict the space<br> domain. In this thesis, after discussing how space object tracking and object position pre-<br> diction is currently being done, I propose a machine learning-based approach to improving<br> the space object position prediction over the standard SGP4 method, which is limited in<br> prediction accuracy time to about 24 hours. Using this approach, we are able to show that<br> meaningful improvements over the standard SGP4 model can be achieved using a machine<br> learning model built based on a type of recurrent neural network called a long short term<br> memory model (LSTM). I also provide distance predictions for 4 different space objects over<br> time frames of 15 and 30 days. Future work in this area is likely to include extending and<br> validating this approach on additional satellites to construct a more general model, testing a<br> wider range of models to determine limits on accuracy across a broad range of time horizons,<br> and proposing similar methods less dependent on antiquated data formats like the TLE.</p>