In the last few years, Dempster–Shafer theory also known as Theory of Belief Functions (TBF) or Evidence theory has received growing attention in many fields of applications such as finance, technology, biomedicine, etc. This theory may be seen as a generalization framework of different instances such as probability, fuzzy sets, and possibility theories. Using Dempster–Shafer belief functions to express available information allows considering two kinds of uncertainty: aleatory uncertainty due to the variability of the variable of interest in the population and epistemic uncertainty due to a lack of knowledge on the state of the variable. Different sources of uncertainty and imprecision may arise in network and telecommunication domains. Such imperfection may be due to imprecision of many aspects regarding the environment: signal, data link, network, etc. For example, it may be due to communication links that might be unreliable, either due to operational tolerance levels or environmental factors. As detailed in the survey paper proposed par Mustapha Reda Senouci, Abdelhamid Mellouk, Mohamed Abdelkrim Senouci, and Latifa Oukhellou in this special issue, the Theory of Belief Functions has proved to be particularly useful to represent and reason with partial information in a wide range of applications, including signal processing, coding, supervision, localization, resource provisioning, etc. In such case, the belief function theory provides a flexible framework for handling and mining imprecision and uncertainty as well as combining different disparate evidence about uncertain events. Indeed, this theory allows modeling different concepts such as imprecision, ambiguity, and ignorance. Also, a variety of combination operators is available in the fusion process. This special issue of Annals of Telecommunications is intended to provide the recent advances on the use of the Theory of Belief Functions and machine learning approaches in telecommunication and network technologies. It focused on how belief functions and machine learning have affected different aspects (protocols, algorithms, paradigm, energy, signal coding, etc.) for a large family of applications (healthcare, medical, underwater, vehicular, robotic, etc.) using network technologies (sensor networks, MANET, VANET, etc.).