Coral reefs are among the most vulnerable ecosystems to coastal and land-based anthropogenic factors. Aside from sudden increase in sea temperatures, external factors such as local and regional disturbances are found to influence coral reef environments which often lead to bleaching events. According to the Status of Coral Reefs of the World: 2020 report, from 2009 to 2018, there has been a progressive loss of live corals at the global level which may be attributed to the increasing anthropogenic activities (Souter et al., 2021). Due to coral’s sensitivity to environmental stressors, it is significantly considered as an indicator for global climate conditions. In this regard, this study developed a remote sensing change detection technique to segment coral bleaching from satellite images. Using a bi-temporal image stack composed of Sentinel-2 images showing pre and post bleaching, a machine learning classification model was developed to capture typologies of changes visible between the two images which included bleaching. Random Forest (RF) algorithm was employed to classify changes. This model obtained overall accuracies and kappa statistics of 0.97 and 0.94 respectively with minimum consumer and producer accuracy of 0.91. Moreover, the identified changes showed 78% agreement with the <em>in-situ</em> data composed of 31 monitoring stations distributed around Sekisei Lagoon, Okinawa, Japan. This study demonstrated a promising potential of machine learning for change detection for coral bleaching monitoring.