The growing demand for high-resolution imagery from Earth Observation (EO) satellites, driven by the need for better environmental monitoring, resource management, and disaster response, necessitates innovative approaches to on-board data compression to optimize downlink transmissions to ground stations. This thesis investigates the application of Artificial Intelligence (AI) techniques for on-board image compression. Unlike traditional algorithms, such as JPEG 2000, which have been the standard for many missions, AI-based models offer the potential for both greater compression efficiency and higher reconstruction quality. This research focuses on developing a lightweight Convolutional Autoencoder (CAE), a type of Convolutional Neural Network (CNN), for single-band image compression capable of operating within the limited resources available to satellite systems.