Abstract: With the depletion of near surface resources in China,deep resource exploitation is the inevitable destiny of the mining industry.Among all mining methods,backfill mining is one of the most important mining methods for deep resource exploitation.The wide application of backfill mining is limited by its high cost.How to increase the efficiency of backfilling design and,thus,reduce backfilling costs is the key to the promotion of mine backfilling.With the development of artificial intelligence (AI) technology,the intelligent algorithms represented by neural networks and decision trees are gradually replacing or assisting humans in simple or complex tasks in various scenarios,which promotes the development of traditional industries.As one of the pillar in traditional industries,the mining industry is trying hard to realize its intellectualization using the state of the art AI methods considering its requirements.Focusing on the application of AI methods in mine backfilling,this paper briefly introduces the basic concepts of AI and the widely used AI methods (including artificial neural network,decision tree,random forest and gradient boosting machine,support vector machine and some unsupervised learning methods).This paper discusses the application difficulties of AI methods in mine backfilling,and systematically analyzes the research status of the application of AI in the flocculation and sedimentation,mix design,rheological properties and pipe transport of slurry,and integrated design and multi objective optimization.This paper also presents some future perspectives of the application of AI in mine backfilling (including performance improvement,small datasets problem and the widening of application ideas) and puts forward,for the first time,the concept of intelligent backfilling system.The development and application AI methods in mine backfilling is of great significance to realize green,intelligent and efficient backfilling design,promote the application of backfilling technology and coordinated exploitation of resources and ecology.