As the title of thesis suggests, this research activity consists in analyzing data traces about video-conferencing tools and developing Machine-Learning approaches for estimating the Quality of Experience of a meeting through measurements on the data traffic. The knowledge of QoE is very valuable in the network providers’ point of view, since by knowing what is the quality experienced at the end user, they can improve the network performance accordingly. The main idea is to use a considerable number of data pcaps, collected from various types of meetings, in order to predict some target QoE metrics aiming to improve the meeting quality.