Dengue fever is the most common mosquito transmitted viral infection afflicting humans, estimated to generate around 390 million infections each year in over 100 countries. The introduction of the endosymbiotic bacterium Wolbachia into Aedes aegypti mosquitoes has the potential to greatly reduce the public health burden of the disease. This approach requires extensive polymerase chain reaction (PCR) testing of the Wolbachia-infection status of mosquitoes in areas where Wolbachia-A. aegypti are released. Here, we report the first example of small organism mid-infrared spectroscopy where we have applied attenuated total reflection Fourier transform infrared (ATR-FT-IR) spectroscopy and multivariate modeling methods to determine sex, age, and the presence of Wolbachia (wMel strain) in laboratory mosquitoes and sex and age in field mosquitoes. The prediction errors using partial least squares discriminant analysis (PLS-DA) discrimination models for laboratory studies on independent test sets ranged from 0 to 3% for age and sex grading and 3% to 5% for Wolbachia infection diagnosis using dry mosquito abdomens while field study results using an artificial neural network yielded a 10% error. The application of FT-IR analysis is inexpensive, easy to use, and portable and shows significant potential to replace the reliance on more expensive and laborious PCR assays.