To solve the problems of energy shortage and waste accumulation, the method of co-combustion of sewage sludge (SS) and peanut shells (PS) was proposed. SS-PS co-combustion characteristics in air were investigated using artificial neural networks (ANN) and thermogravimetric analyses (TGA). The proportion of PS in the mixture was 10%-50%. The temperature range of PS combustion (160-560 degrees C) is lower than that of PS combustion (170-600 degrees C). Activation energy was estimated from two non-isothermal kinetic analysis methods: Kissinger-Akahira-Sunose (KAS) and Flynn-Wall-Ozawa (FWO). The kinetic mechanism of the combustion process was determined by using the master-plots method. Multiple ANN models were established to predict TG data of SS-PS co-combustion. The best prediction model (ANN21) was obtained. The results showed a good overlap between the predicted and experimental TG data. The ANN model and the master-plots method are the main innovations of this study. This study can promote the utilization and reduction of solid waste, and give guidance for the large-scale application of SS-PS co-combustion.