Flow cytometry, a powerful cellular analysis tool, plays a primary role in developing alternative model systems for biomedical research. This chapter explores how flow cytometry aids in characterizing these systems by identifying and evaluating cellular heterogeneity, subpopulations, and surface markers. We delve into applications such as cell sorting for targeted studies, viability and cytotoxicity testing, and cell cycle analysis to understand cell proliferation. Furthermore, the chapter addresses the application of flow cytometry in well-established alternative models, viz., C. elegans, Zebrafish, and Drosophila melanogaster. It describes how flow cytometry helps identify phenotypes, functional indicators, and cellular responses to toxicants. Additionally, the chapter investigates flow cytometry’s role in detecting apoptosis by examining light scattering, mitochondrial potential, and caspase activation. The detection of oxidative stress and reactive oxygen species (ROS) is also explored. The chapter looks ahead to how new types of flow cytometry, such as mass cytometry (CyTOF), image flow cytometry (IFC), and droplet microfluidics, can be combined to improve analysis. Finally, we discuss the emerging role of artificial intelligence (AI) and machine learning (ML) in automating gating strategies, revealing hidden patterns within flow data, and even assisting in model development.