<p>The rapid advancement of artificial intelligence (AI) has revolutionized various scientific domains, particularly in the realm of biomedical research. This transformation is particularly evident in the application of microfluidic impedance flow cytometry (MIFC), a label-free technique that characterizes cells by measuring the electrical resistance changes as they traverse microfluidic channels. Despite its potential to provide detailed cellular information, the large volume and high dimensionality of MIFC data pose significant challenges for traditional data analysis methods. The integration of AI technologies has emerged as a powerful solution, enhancing both the efficiency and accuracy of data analysis. This review provides a comprehensive framework for AI applications in MIFC data analysis, guiding researchers from data acquisition to practical implementation through machine learning workflows. It delves into data acquisition and preprocessing methods, various feature extraction algorithms, and the utilization of machine learning (ML) and deep learning (DL) algorithms for tasks such as cell classification, detection, and prediction. Additionally, this review addresses current challenges and proposes potential solutions, aiming to advance the development of MIFC research.</p>