## 1 Introduction  
The fast and wide spread of Internet of Things (IoT) applications offers new opportunities in multiple domains but also presents new challenges. A skyrocketing number of IoT devices (sensors, actuators, etc.) is deployed to collect critical data and to control environments such as manufacturing, healthcare, urban/built areas, and public safety. At the same time, machine learning (ML) has shown significant success in transforming heterogeneous and complex datasets into coherent output and actionable insights. Thus, the marriage of ML and IoT has a pivotal role in enabling smart environments with precision in decision-making and adaptive automation. However, leveraging ML and IoT still faces significant challenges, obstructing the full realisation of foreseen opportunities. Direct challenges relate to scalability, security, accessibility, resilience, and latency, all of which have resulted in a growing corpus of research addressing one or more of these issues. This Special Issue has attracted leading scholars in this area and offers a view of cutting-edge research centred on the intersection of networks, IoT, and ML. The role of IoT in enabling a smart environment has become indisputable in key verticals that include _Energy_, _Transportation_, _Industrial application_, and _Supply Chain_. The authors of [1] examine the problem of residential energy demand management and the avoidance of collective peak demand. To this end, they use IoT, namely smart meters and appliances, and propose a reinforcement learning method based on a time-of-use tariff that reschedules electric appliances to avoid peak energy demands and, therefore, reduce energy cost. In [2], the problem of road maintenance is investigated, and a solution that leverages IoT (camera-based in this case) to detect and prioritise road damage is proposed. The authors use deep learning (DL) computer vision to segment images and classify the types of damages detected as single cracks, crocodile cracks, and potholes. This information, together with the number of reported damages, is used to prioritise the road maintenance work to improve road safety and reduce emissions by addressing the most damaging road faults. Industrial applications, particularly the supply chain, are addressed in [3], where an IoT-based (in this case, Radio Frequency Identification (RFID)) solution is proposed to detect food or beverage contamination that may take place at the production phase or during the transportation of goods. An ensemble ML approach, XGBoost, is proposed to mine the RFID's received signal strength indicator (RSSI) and detect the presence and level of contamination. Jointly, these studies demonstrate the disruptive potential of merging IoT and ML to solve real-life problems. However, there are major challenges in the design and implementations of ML-based IoT solutions; these include _connectivity_, _security_, and _privacy_. A dominant challenge in this area stems from the exponential increase in IoT devices and the resulting massive number of data generated. In this context, the connectivity of the IoT devices is not a trivial problem when combined with the already crowded wireless and connected networks. This problem is examined from different angles in this Special Issue. The authors of [4] investigate indoor private networks that cater, among other usage, to the continuous monitoring of IoT devices, which causes network overload. In this regard, they propose an ML method for predicting the position of IoT devices and, therefore, reducingthe need for continuous monitoring. Different ML methods are employed, including Random Forest and Artificial Neural Networks, which succeed in predicting the intermediate coordinates of the trajectories on the server-side without relevant data loss.