¹Selcuk University, Faculty of Veterinary Medicine, Biostatistics Department, TR-42130 Konya - TÜRKİYE
²Selcuk University, Faculty of Science, Statistics Department, TR-42130 Konya - TÜRKİYE DOI : 10.9775/kvfd.2025.35867 This study examines 45,769 veterinary medicine publication titles from Türkiye (1980–2024) using large-scale text mining, hybrid clustering, and Sustainable Development Goals (SDG) mapping. It aims to identify long-term thematic trends and assess alignment with global sustainability priorities. A two-stage hybrid clustering approach (k-means + hierarchical) revealed 11 thematic groups. SDG alignment was evaluated using a hybrid Aurora-Elsevier dictionary model enhanced with n-gram-based weighting and validation. Findings indicate a clear shift from traditional species- and production-focused research toward molecular, experimental, and data-driven domains. SDG mapping shows strong associations with Zero Hunger (SDG 2), Good Health and Well-being (SDG 3), Responsible Consumption and Production (SDG 12), and Life Below Water (SDG 14). In contrast, Climate Action (SDG 13) and Life on Land (SDG 15) remain underrepresented, highlighting critical gaps in environmental sustainability. Overall, veterinary research in Türkiye aligns with global production- and health-oriented trends but exhibits partial thematic divergence, particularly in aquatic systems. The comparatively lower emphasis on climate change, biodiversity, and ecosystem health suggests areas that could be further strengthened within the national research agenda. These results offer a data-driven basis for strengthening interdisciplinary research, advancing the environmental dimension of One Health, and improving alignment with global sustainability priorities. Keywords : Hybrid clustering, One health, Sustainable development goals, Text mining, Türkiye, Veterinary medicine