With the proliferation of Internet of things devices, guaranteeing the security of these networked systems has become a top priority. Cyberattacks on IoT devices pose considerable risks to
individuals and companies because they generate massive amounts of sensitive data across numerous linked devices, making data privacy and integrity a key concern. Machine learning models can help classify different types of cyber-attacks in IoT networks based on logs of activities, analyze behaviors, and predict malicious or unusual activities. This research employs a parallel method utilizing Machine Learning techniques such as LDA, SVM, SVM+LDA, and QDA, on the WUSTL-IIOT database and compares it with traditional methods. The data is partitioned into smaller training datasets and trained in parallel. Experiments show that this parallel training system detects and forecasts cyber threats more accurately. The detection speed with the parallel ML models was high, and the best accuracy was 100% using the SVM+LDA model.