Sickle cell anemia (SCA), a genetic disorder caused by the deformation of red blood cells due to hemoglobin S, constitutes a critical global public health challenge, particularly in areas with a high incidence of malaria and Afro-descendant populations such as the Colombian Caribbean. Traditional diagnostic methods, such as hemoglobin electrophoresis, face economic and logistical obstacles in rural and low-income areas. This work proposes an innovative system that combines medical ontologies with knowledge-based models, such as artificial intelligence, to improve the diagnosis and management of the disease. The developed system uses an ontological model structured into domains such as clinical phenotypes, treatments, genetics (HBB gene mutations), and quality of life. The architecture consists of five layers: ontology processing (OWLAPI), triple database storage (RDF), inference engines (SWRL rules), query interfaces, and data visualization. The results demonstrate the usefulness of semantic rules for alerting patients about disease-modifying factors, monitoring patients’ quality of life, and adjusting treatments. The proposal prioritizes accessibility in underserved regions, minimizing dependence on expensive equipment and specialized personnel, which could transform care in resource-limited settings.