Bridging technology and epidemiology: artificial intelligence-driven innovations in mosquito and mosquito-borne disease research

Mosquito-borne diseases remain a major global health concern, affecting millions annually and posing persistent challenges for surveillance, prediction, and control. The integration of artificial intelligence (AI) into entomological and epidemiological research offers transformative opportunities to bridge technology and public health. This review highlights recent advances in AI-driven approaches across mosquito-borne disease research. In mosquito surveillance, AI enhances mosquito species identification through computer vision, acoustic, and spectroscopic techniques, enabling automated and high-throughput recognition of mosquitoes. Machine learning and deep learning applications also deepen insights into mosquito biology, ecology, and spatial distribution, supporting optimized control strategies. In disease prediction and early warning systems, AI has evolved from traditional machine learning to deep learning models that integrate climatic and demographic data, while explainable AI improves transparency and interpretability. Beyond surveillance and forecasting, AI supports integrated mosquito management through precision interventions and real-time data analysis. Its role in public health communication also promotes community engagement and risk awareness via digital platforms. Despite these advances, key challenges remain, including data heterogeneity, limited system integration, ethical issues, and the need for interdisciplinary collaboration. Addressing these gaps is essential to fully harness AI’s potential in strengthening global responses to mosquito-borne diseases and advancing predictive epidemiology.