  The Internet of Things (IoT) has evolved from a novel technology to an
integral part of our everyday lives. It encompasses a multitude of
heterogeneous devices that collect valuable data through various sensors. The
sheer volume of these interconnected devices poses significant challenges as
IoT provides complex network services with diverse requirements on a shared
infrastructure. Network softwarization could help address these issues as it
has emerged as a paradigm that enhances traditional networking by decoupling
hardware from software and leveraging enabling technologies such as Software
Defined Networking (SDN) and Network Function Virtualization (NFV). In
networking, Machine Learning (ML) has demonstrated impressive results across
multiple domains. By smoothly integrating with network softwarization, ML plays
a pivotal role in building efficient and intelligent IoT networks. This paper
explores the fundamentals of IoT, network softwarization, and ML, while
reviewing the latest advances in ML-enabled network softwarization for IoT.
