The mosquito is a vector for human arboviruses and zoonotic diseases, such as yellow fever, dengue, Zika, and chikungunya, and as such poses a serious threat to public health. Understanding how adapts to environmental pressures-such as insecticides-is critical for developing effective mitigation strategies. However, most traditional methods for detecting recent positive selection search for signatures of classic "hard" selective sweeps, and to date no studies have examined soft sweeps in This represents a significant limitation as this is vital information for understanding the pace at which an organism can adapt-populations that are able to immediately respond to new selective pressures are expected to adapt more often via standing variation or recurrent adaptive mutations (both of which may produce soft sweeps) than via mutations (which produces hard sweeps). To this end, we used a machine learning method capable of detecting hard and soft sweeps to investigate positive selection in population samples from Africa and the Americas. Our results reveal that soft sweep signatures are significantly more common than hard sweeps in all population samples, including those that have experienced population bottlenecks, which may imply that this species can respond quickly to environmental stressors. This is a particularly concerning finding for vector control methods that aim to eradicate through the use of insecticides. We highlight genes under selection that include both well-characterized and putatively novel insecticide resistance genes. These findings underscore the importance of using methods capable of detecting and distinguishing hard and soft sweeps, implicate soft sweeps as a major selective mode in and highlight genes that may aid in the control of populations.