The Internet of Things (IoT) is a crucial driver of the fourth industrial revolution, enabling devices to operate intelligently without human intervention. IoT devices, including sensors, smart devices, and RFID systems, collect and transmit data over the Internet. However, IoT security remains a major challenge due to the vast number of connected devices and their limited computing power, making them vulnerable to cyberattacks. Traditional cryptographic security measures often fail to fully address IoT security needs. Given these limitations, machine learning (ML) techniques have emerged as a promising solution to enhance IoT security by embedding intelligence into devices and networks. ML-based approaches can detect and mitigate security threats more effectively than conventional methods, offering new research opportunities. This paper reviews various IoT attacks, explores ML techniques to counter these threats, and provides recommendations for future research in securing IoT networks.