The article describes a model of the indoor climate control process, as well as structural diagrams of control modules based on neuro-fuzzy modeling. The developed model of intelligent climate control uses the value of the thermal comfort index PMV to control the values of temperature and humidity in the room. A mathematical model and algorithms for this model are presented. A two-layer neural network will allow you to predict the PMV index with sufficient accuracy, especially on data not from the training set. The fuzzy logic module uses a database of 33 rules for converting information from sensors (temperature and humidity) and a neural network into fuzzy information to regulate temperature and humidity values based on the value of the PMV comfort index. To take into account the dependence of the room temperature on the external temperature and the heater temperature, an additional Tdif module was introduced, which allowed to increase the efficiency of the model. Computational experiments have shown that based on this model, it is possible to obtain the economic efficiency of the indoor climate control system.