Acute kidney injury (AKI) is a complex and multifactorial syndrome characterized by the rapid decline of renal function following ischemic, septic, traumatic, hypoxic, or nephrotoxic insults. Despite advances in critical care, exacerbation of AKI is associated with substantial morbidity, mortality, progression to chronic kidney disease (CKD) and multiorgan dysfunction. Current therapies remain largely supportive and do not directly target the molecular pathways driving AKI, underscoring the need for mechanism-based diagnostics and precision-guided therapeutic strategies. Accumulating evidence supports that AKI is driven by mechanistic crosstalk among cellular injury pathways involving mitochondrial dysfunction, oxidative stress, endothelial damage, immune dysregulation, and regulated cell death. Flow cytometry has emerged as a powerful platform for high-dimensional single-cell and extracellular vesicle analysis, enabling comprehensive assessment of immune cell phenotypes, platelet activation, endothelial alterations, mitochondrial dysfunction, and circulating extracellular vesicles associated with AKI. Advances in artificial intelligence (AI), machine learning and emerging explainable AI (XAI) enhance the ability to identify clinically relevant patterns from complex datasets. This review aims to integrate AKI pathophysiology with flow cytometry-based biomarker discovery and AI-enabled analytical approaches. We discuss translational applications in AKI-relevant clinical settings, including trauma, hemorrhagic shock, sepsis, and radiation-combined injury, with particular emphasis on Prolonged Casualty Care and other austere environments, where rapid, mechanism-based diagnostics are critically needed.