Vector-borne diseases remain a pressing global health challenge exacerbated by climate change, urbanization, and insecticide resistance. This review evaluates how artificial intelligence can strengthen vector surveillance, forecast outbreaks, and optimize interventions in public-health programs. Traditional control strategies rely on reactive. AI-driven systems, such as convolutional neural networks (CNNs, a form of image-recognition AI) and advanced machine learning models, achieve over 90% accuracy in real-time mosquito species identification, enabling targeted responses to invasive vectors such as Anopheles stephensi and Aedes aegypti. The integration of satellite imagery, climate data, and citizen science inputs into machine learning models improves outbreak prediction, with Bayesian networks forecasting dengue incidence 30 d in advance with 81% accuracy. AI also streamlines resource allocation and reduces insecticide use by 20-40% through reinforcement learning and drone-based habitat mapping. However, barriers to implementation persist, including data inequities, algorithmic biases, and infrastructure gaps in low-income regions. Ethical considerations such as privacy in data-sharing systems and community engagement in surveillance necessitate collaborative frameworks that bridge technologists, public health experts, and local stakeholders. Although AI cannot replace traditional methods, its capacity to augment decision-making in real time and data-driven insights offer a pathway for resilient, equitable vector control systems. Success depends on context-specific adaptation, investment in digital infrastructure, and sustained cross-sector partnerships to mitigate the disproportionate burden of vector-borne diseases on vulnerable populations.
Abdi et al. (2025) studied this question.