In the fields of data analytics and industrial automation, the Internet of Things (IoT) has become a game-changer. In IoT contexts, there is a growing demand for effective use of resources due to the interconnection of devices and systems. The constantly changing character of IoT systems, where resource availability and demand alter continually, presents one of the main obstacles in resource allocation management. The capacity of machine learning approaches to manage intricate and changing structures has garnered substantial interest in recent times. In the framework of the Industrial Internet of Things, this work gives a detailed comparison of various resource allocation in IoT network using machine learning algorithms.