The object of the research is the Internet of Things system and its component, the Smart Home system. The subject of the research is machine learning methods and neural networks. The purpose of this work is to study methods for predicting climate control indicators (air temperature, humidity level, air conditioning, air filtration) for the development of a "Smart Home" system that unites all devices in the room (air conditioners, split systems, underfloor heating, radiators) into a single network This ensures control over the process of their interaction, increases the level of comfort for residents and guarantees significant energy savings. Research methods ̶ methods of system and object-oriented analysis, machine learning methods. To implementation the set goal, the analysis of Internet of Things and Smart Home technologies was carried out in the work; devices and their indicators to ensure climate control in smart home systems. The use of neural networks for solving the problem of predicting climate control indicators in smart home systems has been substantiated. As the apxitecture of the artificial neural network, a multilayer perceptron with feedback was chosen; reinforcement learning method is used; the backpropagation method was chosen as the learning algorithm. A control system based on a neural network was presented to create a Smart Home system. Simulation modeling of the presented neural network using the Matlab environment and software implementation of the system using Java have been performed. The paper presents a model of an artificial neural network, which makes it possible to increase the efficiency of the climate control system in a smart home due to more stable results when making forecasts of dynamic environmental indicators. Simulation modeling of the developed artificial neural network using the Matlab environment and software implementation of the system using Java have been performed.