Background: Acute kidney injury (AKI) is a critical clinical syndrome characterized by a rapid decline in renal function, with various precipitating factors including heart failure, sepsis, and nephrotoxic drugs. The prevalence in hospitalized patients is concerning, particularly among those with COVID-19, where AKI incidence has reached approximately 36.6%. The current diagnostic criteria primarily rely on serum creatinine (SCR) levels and urine output (UO), which often fail to identify AKI early enough for effective intervention. Aim: This review aims to consolidate current knowledge on AKI, highlighting its diagnosis, causes, and the latest treatment approaches, with a focus on emerging technologies that improve early detection. Methods: The article reviews literature on AKI diagnostic criteria, imaging techniques, biomarkers, and the application of machine learning algorithms in predicting AKI. Emphasis is placed on novel biomarkers and biosensors that enhance early detection, as well as machine learning models that synthesize data from electronic health records. Results: Advances in biomarkers like NGAL and KIM-1, alongside biosensors, offer improved sensitivity for early AKI detection. Additionally, machine learning models have demonstrated high predictive accuracy, achieving area under the receiver operating characteristic curve (AUC) values exceeding 0.9 across various clinical contexts.