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Large-scale egg farming faces challenges in assessing the health status of laying hens and preventing disease outbreaks. The need for effective flock health monitoring in egg production is becoming increasingly important. As homeothermic animals, the body temperature of laying hens serves as a crucial indicator of their health. A method for measuring the body temperature of stacked cage laying hens was proposed by integrating multi-source information. To improve measurement accuracy, temperature drift correction and distance correction were applied to the thermal infrared camera. The thermal infrared images were then pixel-level aligned with the acquired near-infrared and depth images. These fused multi-source images were used to detect the heads of the laying hens through the YOLO v8n detection network, achieving detection results of 97.0% for AP50 and 76.1% for AP50-95. Temperature drift and distance corrections were performed on the thermal infrared images of the hens’ heads, using ambient temperature and distance information. Temperature feature points were then extracted from the corrected images to calculate the head temperature of the laying hens. A prediction dataset was constructed based on environmental factors such as ambient temperature, humidity, wind speed, light intensity, and the hens’ head temperature. Various machine learning algorithms were used to predict the body temperature, with the random forest algorithm showing the best performance, achieving an R2 of 0.696 and an RMSE of 0.246℃. The research result can provide a reference for achieving accurate, high-throughput, and non-invasive measurement of body temperature in large-scale egg farms.