The complex icy surface of Jupiter's moon, Europa, has long fascinated planetary science and astrobiology communities. NASA spacecraft observations of Europa have revealed an enigmatic 'chaos terrain,' characterized by jigsaw-like areas of broken ice blocks caused by significant past subsurface disruption events. Speculation suggests the ice crust in these regions may be thinner, potentially offering better access to a warm ocean that may harbor complex organic compounds. These regions are favorable targets for future solar system missions, and may offer additional insight into Europa's internal processes. Although substantial progress has been made in visually cataloging chaos terrain, the precise mapping of ice blocks is laborious, subjective, and resource-intensive. Leveraging the capabilities of machine learning (ML) algorithms to expedite and automate such tasks will be crucial to scale this effort to other solar system bodies. To address this, we explore using a Mask R-CNN and transfer learning to detect and segment individual ice blocks within chaos terrain. Our current model achieves a highest precision score of 71.8% and recall score of 67.6%. We present the current strengths and limitations of our model and dataset while outlining avenues for further improvement. This work aims to contribute to future mission planning for Europa and other solar system bodies. Additionally, it highlights the unique algorithmic challenges posed by planetary science data and emphasizes the need for innovative ML solutions.