Background Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance. Methods A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification. Results The best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus. Conclusions To our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations. Graphical abstract