Sepsis-associated acute kidney injury (SA-AKI) is a significant complication of sepsis, affecting up to 70% of cases. SA-AKI is defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria and is categorized into early (first 48 hours) and late (48 hours to 7 days) forms. It is associated with worse prognosis compared to sepsis or AKI alone. The pathogenesis of SA-AKI involves multiple mechanisms, including inflammation, metabolic reprogramming, various types of cell death (apoptosis, necroptosis, pyroptosis, ferroptosis), autophagy, efferocytosis, and hemodynamic changes. Diagnostic biomarkers such as interleukins, osteoprotegerin, galectin-3, presepsin, cystatin C, NGAL, proenkephalin A, CCL-14, TIMP-2, and L-FABP have been identified. Machine learning algorithms and multi-omics technologies are being used to develop more accurate diagnostic and prognostic tools. Therapeutic approaches focus on fluid resuscitation, antibiotic administration, and targeted interventions based on the underlying pathophysiology. The article reviews the current understanding of SA-AKI, its pathogenetic mechanisms, and potential therapeutic strategies, emphasizing the need for personalized management based on multi-omics studies and bioinformatics.Sepsis-associated acute kidney injury (SA-AKI) is a significant complication of sepsis, affecting up to 70% of cases. SA-AKI is defined by the Kidney Disease: Improving Global Outcomes (KDIGO) criteria and is categorized into early (first 48 hours) and late (48 hours to 7 days) forms. It is associated with worse prognosis compared to sepsis or AKI alone. The pathogenesis of SA-AKI involves multiple mechanisms, including inflammation, metabolic reprogramming, various types of cell death (apoptosis, necroptosis, pyroptosis, ferroptosis), autophagy, efferocytosis, and hemodynamic changes. Diagnostic biomarkers such as interleukins, osteoprotegerin, galectin-3, presepsin, cystatin C, NGAL, proenkephalin A, CCL-14, TIMP-2, and L-FABP have been identified. Machine learning algorithms and multi-omics technologies are being used to develop more accurate diagnostic and prognostic tools. Therapeutic approaches focus on fluid resuscitation, antibiotic administration, and targeted interventions based on the underlying pathophysiology. The article reviews the current understanding of SA-AKI, its pathogenetic mechanisms, and potential therapeutic strategies, emphasizing the need for personalized management based on multi-omics studies and bioinformatics.