Trust is one of the essential constituents in network security systems, and trust management is the major challenging issue in the Internet of Things (IoT). IoT intelligently admits abundance of devices in many real-world applications to interchange huge data with others, in which this data contains confidential and sensitive information of the participants such as locations, interests, and activities. It enforces trust management issues due to the risk of delicate data exchange and the perception of uncertainty.
   Consequently, it is requisite to build an intelligent trust computational model for IoT services to understand the capabilities and limitations of risk in decision making, and uncertainties produce accurate and intuitive trust values meant for variegated influential factors. The effective trust computational model is developed by utilizing a machine learning model to perform classification and feature extraction of trust values to make proper decisions. An intelligent trust computation model using a machine learning algorithm predicts the trustworthy and reliable inter-communications in IoT applications. The applicability of trust management technology in several domains, including economics, sociology, and computer science.