The exploration of marine plants as sources of novel anticancer compounds has gained significant attention due to their diverse biochemical compositions and unique ecological adaptations. Among these marine organisms, Sargassum spp. stands out for its rich bioactive profile and potential therapeutic applications in oncology. This abstract provides an overview of the recent advancements in Artificial Intelligence (AI)-guided discovery of anticancer compounds from Sargassum spp., emphasizing the integration of computational approaches to accelerate drug discovery processes. Sargassum spp., a genus of brown algae widely distributed in coastal regions and oceanic currents, has been traditionally used in folk medicine and increasingly studied for its pharmacological properties. AI technologies, including machine learning and molecular docking simulations, have revolutionized the identification of bioactive compounds from natural sources. By leveraging large-scale datasets from chemical databases, bioactivity assays, and scientific literature, AI facilitates the efficient screening and prediction of potential anticancer agents within Sargassum spp. The AI-guided discovery process begins with data collection and preprocessing, where diverse chemical structures and biological activities of Sargassum spp. compounds are curated and standardized. Predictive models are developed using quantitative structure-activity relationships (QSAR) and molecular docking studies, leveraging machine learning algorithms. These models evaluate how compounds interact with cancer-specific targets, forecast their therapeutic effectiveness, and enhance their drug-like properties.