In this chapter, we aim at the current status of monitoring transmission towers with manual and UAV as the mainstream. The application of deep learning in power systems is studied to solve some difficult problems in the condition monitoring of transmission towers. In this chapter, the dataset collected in the transmission tower has been preprocessed to keep the dataset samples balanced. Thus, improve the accuracy of prediction. At the same time, this chapter evaluates the algorithms with accuracy rate, accuracy rate, recall rate, F1 score, macro AVG, and weighted AVG in consideration of their own characteristics. Through the application of mainstream classification algorithms in the transmission tower condition monitoring environment, the algorithm that is most suitable for the dataset of the transmission tower environment is found, and the theoretical foundation is laid for the online monitoring of the transmission tower. The main classification algorithms used in this chapter are the gradient boosted decision tree (GBDT), K-means, SVM, Naive Bayes, random forest, and fuzzy inference system.