A dual parameter fault warning model for dry-type transformers in distribution networks was proposed in this paper. The model is based on the LSTM-GMM algorithm and aims to address the challenges posed by limited monitoring data types and the difficulty in fault warning caused by limited monitoring methods. Firstly, the monitoring data was preprocessed, and feature values strongly related to the temperature of the transformer winding were extracted, which were used as inputs for the long short term memory (LSTM) network. Then, the hyperparameters of the LSTM network were optimized, and the network was trained with the winding temperature as the target variable. The residual set was calculated based on the predicted values output by the trained network. A Gaussian mixture model (GMM) was employed to determine the probability density distribution of the residual set, and the fault criterion was defined using the confidence interval to classify the warning level. Finally, the warning level was converted into a failure rate. The analysis of winding fault data for dry-type transformers in residential areas of Yifengyuan, Beijing that the proposed dual parameter warning model exhibits better predictive performance compared to algorithms such as backpropagation neural networks and can achieve a fault warning 3 hours in advance.