Abstract: In recent years, artificial intelligence (AI) technology has witnessed tremendous progress, particularly in the field of engineering applications. From simple machine learning (ML) models and deep learning (DL) equipped with complex image processing capabilities, to the rapidly advancing large language models (LLMs), all these models have demonstrated formidable capabilities in engineering applications. Microfluidics, as a crucial technology in chemical synthesis and the life sciences, also requires the assistance of AI technology. The integration of artificial intelligence with microfluidic technology has emerged as a significant trend in the field of microfluidics. The combination of AI's powerful data processing capabilities with the high-throughput generation, high-precision controllability, and rapid reaction analysis capabilities of microfluidics offers a powerful toolkit for fields such as materials science, chemical reaction, and biomedicine. Compared to traditional manual analysis methods, AI-assisted microfluidic technology provides faster processing speeds and reduces human intervention significantly, addressing the challenges of conventional microfluidics, such as reliance on researcher experience, poor experimental repeatability, and time-consuming optimization processes. Simultaneously, AI models can further facilitate investigations into fundamental microfluidic principles. This represents a highly promising direction, yet literature is scarce in systematically summarizing and elaborating upon this emerging interdisciplinary field. Accordingly, this paper systematically reviews the research progress of AI-assisted microfluidic technology. We start with introducing the AI models, which are employed in the microfluidics domain, including four common machine learning models, tree-based models and support vector machines (SVM), deep learning (DL), and reinforcement learning (RL). Tree models typically possess strong interpretability. SVM is a common classification model suitable for complex data. DL is a frequently used image processing model that achieves functions such as object detection and image classification by simulating the human brain via neural networks, and RL is a distinct trial-and-error AI algorithm that continuously enhances its own performance through interaction with the environment. Following, we discuss the applications of AI-assisted microfluidic technology from several aspects: microfluidic droplet generation, microreactor optimization design, micro/nano-material synthesis, catalytic reactions, and biomedical detection. Regarding droplet microfluidics, we examine how AI models predict generation performance and employ explainable frameworks to investigate the underlying factors governing these processes. We further showcase the use of deep learning for flow pattern recognition and the real-time tracking of droplets and bubbles. Turning to microreactors, we analyze the integration of machine learning for structural design and performance optimization. In the realm of micro/nano-material synthesis, we explore AI-driven approaches for property prediction and the construction of autonomous synthesis platforms. Furthermore, in chemical reactions field, we discuss the application of AI to identify optimal reaction conditions and enhance substance detection. Finally, we discuss the advancement of AI in cell sorting, high-precision detection, and the forecasting of cellular changes within the biomedical field. In conclusion, this review provides an outlook on the future development directions of this interdisciplinary field. In the outlook section, we propose three perspectives: the development of efficient label-free, semi-supervised models and high-precision models suitable for small datasets; the creation of end-to-end AI models that bypass complex feature extraction steps; and the integration of AI models with integrated microfluidic chips to develop automated platforms and realize innovative microfluidic application modes.