University of Tabriz Journal of Zoonotic Diseases 2476-535X 10 3 2026 08 01 Artificial intelligence in zoonotic disease surveillance: A systematic review with One Health implications for low- and middle-income countries 1285 1295 22120 10.22034/jzd.2026.22120 EN Kunta Adnan Sahiman Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Muhammad Halim Natsir Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Kuswati Kuswati Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Budi Hartono Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Nurul Isnaini Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Herly Evanuarini Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia 0000-0001-5080-8858 Andi Moh Iekam Faculty of Veterinary Medicine, Universitas Airlangga, Surabaya, Indonesia Siti Azizah Department of Animal Science, Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia Journal Article 2026 06 07 Zoonotic diseases account for approximately 60% of known infectious diseases and 75% of emerging threats, disproportionately affecting low- and middle-income countries (LMICs). This systematic review synthesises evidence on artificial intelligence (AI) applications in veterinary surveillance relevant to zoonotic disease monitoring, with attention to LMIC implementation contexts. Following PRISMA 2020 guidelines and JBI methodology, we searched PubMed, Scopus, and Web of Science (January 2015–December 2025). Two reviewers independently performed screening, full-text assessment, and data extraction. Methodological quality was appraised using a six-item framework integrating JBI and PROBAST elements. Thirty empirical studies met inclusion criteria (20 primary empirical + 10 descriptive operational evaluations); 50.0% addressed LMIC settings. Four application domains were identified: predictive surveillance (30.0%), event-based surveillance (23.3%), automated monitoring (23.3%), and disease detection (23.3%). Fourteen studies (46.7%) demonstrated direct zoonotic relevance. A machine learning rabies risk-stratification approach yielded an approximately three-fold increase in epidemiologically useful surveillance data, and event-based syndromic classification achieved F1-macro of 0.9995 under retrospective validation. Edge AI and transfer learning approaches demonstrated feasibility for resource-constrained settings. AI-enabled approaches may strengthen zoonotic disease surveillance within One Health frameworks, particularly in LMIC settings, though most evidence reflects proof-of-concept and retrospective validation; prospective operational research is needed. https://jzd.tabrizu.ac.ir/article_22120_652e6745d142e8581cbd1662abb41b49.pdf