Abstract: In recent years, artificial intelligence (AI) has witnessed tremendous progress, particularly in the field of engineering applications. From simple machine learning (ML) models and deep learning (DL) models equipped with complex image processing capabilities to the rapidly advancing large language models, these have demonstrated formidable capabilities in engineering applications. Microfluidics, a crucial technology in chemical synthesis and life sciences, integrated with AI 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 provides 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 reduced human intervention to address the challenges in conventional microfluidics, such as reliance on researcher experience, poor experimental repeatability, and time-consuming optimization processes. Simultaneously, AI models can facilitate investigations into fundamental microfluidic principles. This represents a highly promising direction, yet literature that systematically summarizes and elaborates on this emerging interdisciplinary field remains scarce. This paper systematically reviews the research progress of AI-assisted microfluidic technology. We start by introducing the AI models, which are employed in the microfluidics domain, including four common ML models: tree-based models, support vector machines (SVM), DL, and reinforcement learning (RL). Tree models typically possess strong interpretability. SVM is a common classification model suitable for complex data. DL models are frequently used image processing models that achieve functions such as object detection and image classification by simulating the human brain via neural networks. RL is a distinct trial-and-error AI algorithm that continuously enhances its own performance through interaction with the environment. Next, we discuss the applications of AI-assisted microfluidic technology from several aspects: microfluidic droplet generation, microreactor optimization design, micro/nanomaterial synthesis, catalytic reactions, and biomedical detection. Regarding droplet microfluidics, we examine how AI models predict the generation performance and employ explainable frameworks to investigate the underlying factors governing these processes. We showcase the use of DL for flow pattern recognition and the real-time tracking of droplets and bubbles. Turning to microreactors, we analyze the integration of ML for structural design and performance optimization. In the realm of micro/nanomaterial synthesis, we explore AI-driven approaches for performance prediction and the construction of autonomous synthesis platforms. In the field of chemical reactions, 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 of this interdisciplinary field. We propose four perspectives: the expand of standardized protocols and highly versatile interfaces, 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.