Vector-borne diseases have risen substantially in incidence across the United States over the past two decades, driven largely by expanding vector ranges, shifting climate conditions, urbanization, and socioeconomic vulnerabilities. Diseases such as dengue and spotted fever group Rickettsioses are now emerging or re-emerging in regions previously considered low risk. These trends are projected to intensify as climate change continues to alter habitat suitability, seasonality, and transmission dynamics. The public health infrastructure responsible for vector surveillance and control remains chronically under-resourced, fragmented, and reliant on labor-intensive methods that cannot keep pace with rapidly changing risk landscapes. There is a critical need for scalable, proactive, and data-driven tools that can detect, predict, and interrupt transmission before outbreaks occur. This presentation will highlight our work to develop innovative technologies to strengthen vector-borne disease surveillance and control. Specifically, we will discuss the integration of artificial intelligence and machine learning to optimize vector surveillance, enhance early-warning systems, and inform targeted control strategies.