Microbial data can be one of the most resourceful sources of information on life. Every possibile environmental niche on the planet has now been colonised by bacterial life, and this includes larger communities like insects. Nowadays is common for microbial data to being used in a variety of methods to deepen our knowledge of insects, however one of the practices that is becoming more and more common is using these kind of data for operating some sort of "control" on communities of pests, and vectors. Malaria, is still one of the burdens weighting down on developing countries, and despite the existence of treatments, they are costly. Moreover, many more dangerous pathogens like Zika, Yellow Fever, they all spread exploiting vector mosquitoes. Thus, preventing the spread of this diseases is one of the most important challenges today, and adopting new methodologies other than the one currently in use can help to further prevent the spread of Malaria. The aim of this project is to use NGS data from Illumina platform, for analysing the microbiota of vector mosquitoes and exploring possibile patterns using data analysis techniques and machine learning approaches.