Currently, artificial intelligence methods are undergoing accelerated development and widespread application in various areas of human activity, including Kohonen neural networks. The task of continuous monitoring and timely response to natural and man-made emergencies is still urgent. In this study, it is proposed to use Kohonen neural networks, a U-shaped network, and static algorithms to solve it using the example of an accident on power supply networks due to the effects of meteorological factors. To create the database, a list of factors influencing the power line breakage was formed. The development of an artificial neural network consisted of preparing data for training a neural network, creating a Kohonen neural network, testing and validating the created neural network on a test dataset, evaluating the accuracy of its results, and using it to predict emergencies. The use of a U-shaped network made it possible to obtain reports for different dates under different meteorological conditions. Using the Kohonen network allowed us to conclude that weather conditions do not always directly affect the likelihood of emergencies. Thus, optimization of the emergency monitoring and response system using neural networks demonstrates a clear advantage over existing systems, as it allows analyzing a large amount of information in a limited time.