We have entered a data-driven era of astrophysics and cosmology, providing a wealth of datasets within which to search for the answers to some of the most fundamental open questions in the physics of our Universe. One of these questions is the nature of dark matter (DM)—while there is phenomenal agreement between the theories of DM and the data on cosmological scales, there remains much to be understood about DM on scales at and smaller than the size of galaxies. This thesis explores the astrophysical and particle physics properties of dark matter in the Milky Way Galaxy. Chapters 2–4 center around indirect detection of DM, the field of research that seeks to detect the Standard Model particles which result from DM annihilation (or decay). The focus here is specifically on searching for signatures of DM annihilation in gamma-ray data from the Fermi Large Area Telescope. Chapters 5–6 are dedicated to understanding substructure in the Milky Way. Chapter 5 focuses on characterizing how well the standard Jeans dynamical mass modeling method performs at accurately capturing the DM content of dwarf galaxies, while Chapter 6 presents a novel machine learning-based approach to inferring the missing information from Gaia stellar data, which can then be used to search for evidence of stellar and DM substructure in the Milky Way.