This study examines the landscape dynamics in the region surrounding Comandante Ferraz Antarctic Station, Keller Peninsula, King George Island, focusing on the quantification of land cover changes over 23 years. Emphasis is placed on the integration of a multitemporal Landsat time series (2001–2024) within a standardized spatio-temporal data cube framework, coupled with a Random Forest (RF) classification approach. This methodology enables consistent pixel-wise trajectory analysis across seven distinct epochs. The RF models achieved robust performance, with F1-scores for dominant classes like water and soil typically exceeding 0.90, although seasonal snow and ice showed greater spectral ambiguity in transitional months. Quantitative results from the transition matrices reveal a significant landscape reconfiguration: while ice (85.3%) and soil (81.2%) showed high persistence, a prominent trend of deglaciation was identified, characterized by the transition of ice and snow into exposed soil and the emergence of pioneer vegetation communities detected from 2014 onwards. The study demonstrates that the integration of machine learning and data cubes provides a powerful tool for monitoring environmental shifts in high-latitude maritime Antarctica, supporting long-term ecological assessments and climate impact modeling.