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Background Malaria is still a major public health issue worldwide, and one of the best approaches to fight the disease remains vector control. The current methods for mosquito identification include morphological methods that are generally time-consuming and require expertise, and molecular methods that require laboratory facilities with relatively expensive running costs. Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS) technology, routinely used for bacterial identification, has recently emerged in the field of entomology. The aim of the present study was to assess whether MALDI-TOF MS could successfully distinguish Anopheles stephensi mosquitoes according to their Plasmodium infection status. Methods C57BL/6 mice experimentally infected with Plasmodium berghei were exposed to An. stephensi bites. For the determination of An. stephensi infection status, mosquito cephalothoraxes were dissected and submitted to mass spectrometry analyses and DNA amplification for molecular analysis. Spectra were grouped according to mosquitoes’ infection status and spectra quality was validated based on intensity and reproducibility within each group. The in-lab MALDI-TOF MS arthropod reference spectra database, upgraded with representative spectra from both groups (infected/non-infected), was subsequently queried blindly with cephalothorax spectra from specimens of both groups. Results The MALDI TOF MS profiles generated from protein extracts prepared from the cephalothorax of An. stephensi allowed distinction between infected and uninfected mosquitoes. Correct classification was obtained in blind test analysis for (79/80) 98.75% of all mosquitoes tested. Only one of 80 specimens, an infected mosquito, was misclassified in the blind test analysis. Conclusions Matrix-Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry appears to be a promising, rapid and reliable tool for the epidemiological surveillance of Anopheles vectors, including their identification and their infection status.