Heat flow is a geothermal parameter for indicating the heat sources distribution and evaluating geothermal reservoirs. Only 1230 heat flow points are distributed unevenly in China, mainly concentrated in the high-temperature geothermal areas and the southeast regions. The Songliao Basin is a potential geothermal field in China. Still, only 20 measurement points are known, making it difficult to evaluate the geothermal genetic mechanism. Sparse data interpolation using deep learning methods have high accuracy and are widely used in fields such as image processing. In this work, we propose a deep neural network for predicting heat flow in the Songliao Basin. More than 4,000 global heat flow and 23 geological and geophysical parameters are used as reference constraints for training. The uncertainty error of the prediction is estimated based on the correlation and distance-based generalized sensitivity analysis. The results show that the maximum heat flow is 85 mW/m2, the average is 67.1 mW/m2, and the error with the measured data is 10.64%. The previous geophysical and geological interpretation results indicate that the heat flow is higher in the west and lower in the east, with high anomalies in the central region, which may be related to the uplift of the deep mantle and the depression of the shallow low-velocity sedimentary layer. Some high-temperature melt bodies are in the deep layers, forming the current potential geothermal field. The measured data validates that the DNN is an effective method for predicting regional-scale heat flow, providing reliable heat source information for evaluating geothermal resources.