The tomato leaf miner, Tuta absoluta (Meyrick), also known as Phthorimaea absoluta, is a significant polyphagous invasive agricultural pest. It primarily infests Solanaceae crops, such as tomatoes and potatoes, and can cause tomato yield losses of 50%~80%, or even complete crop failure. Therefore, as the "Ebola virus of tomatoes", T. absoluta was first found in Xinjiang, China, in 2017, and has broken out in several regions of China. T. absoluta often occurs covertly, and its suitable habitats are widespread in China. This pest can transmitted over long distances through the transport of tomato seedlings and fruits, indicating a clear trend of further spread.Therefore, there is an urgent need to develop efficient and low-cost detection and identification technologies and methods for T. absoluta, in order to achieve early detection and advance warning of this significant invasive species.Currently, the primary techniques used to identify T. absoluta include traditional morphological identification methods, molecular biological detection techniques, and artificial intelligence-based image recognition methods. In this paper, the research status of detection and identification technology of T. absoluta was reviewed, and its development trend was prospected, aiming at providing technical support for delaying the spread and diffusion of T. absoluta and targeting strategies.