The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing. The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent networks. The issue of security in the realm of the Internet of Things (IoT) has emerged as a significant concern. The security of the Internet of Things (IoT) is compromised by the potential danger posed by exploiting devices connected to the Internet. The susceptibility of Things to botnets poses a significant threat to the entire Internet ecosystem (smart devices). In recent years, there has been a simultaneous evolution in the complexity and variety of security attack vectors. Therefore, it is imperative to analyse IoT methodologies to detect and alleviate emerging security breaches. The present study analyses network datasets, distinguishing between those of the Internet of Things (IoT) and those that do not, and provides a thorough overview of the findings. Our primary focus is on IoT Network Intrusion Detection (NID) studies, wherein we examine the available datasets, tools, and machine learning (ML) techniques employed in the implementation of network intrusion detection (NID). Subsequently, an evaluation, assessment, and summary of the current state-of-the-art research on IoT-related Network Intrusion Detection (NID) conducted between 2018 and 2024 is presented. This includes an analysis of the publication year, dataset, attack types, experiment results, and the advantages, disadvantages, and classifiers employed in the studies. This review emphasises research related to IoT NID that employs Supervised Machine Learning classifiers, owing to the high success rate of such classifiers in security and privacy domains. Furthermore, this survey incorporates a comprehensive analysis of research endeavours on IoT NID. Furthermore, we have identified publicly available IoT datasets that can be utilised for NID experiments, which would benefit academic and industrial research purposes.