This study aimed to classify intensive care unit (ICU) patients who developed acute kidney injury (AKI) into clusters based on serum creatinine trajectories and clinical characteristics, and to assess differences in outcomes across clusters. In this retrospective cohort study using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, ICU patients who developed AKI (defined per KDIGO criteria) were included. Patients with end-stage kidney disease or prior kidney replacement therapy (KRT) were excluded. We collected creatinine measurements (7 days prior, 4 days after AKI onset), demographics, vital signs, comorbidities, nephrotoxin and vasopressor use, sepsis, cardiac surgery history, and laboratory results. Missing data were imputed using multiple imputation and linear interpolation. Clusters were generated via deep learning-based autoencoders and Deep Embedded K-Means clustering. The optimal number of clusters was selected using the Davies-Bouldin index. Clinical outcomes, including mortality and KRT initiation at 7, 14, and 30 days post-AKI, were compared across clusters. A total of 30,096 ICU patients with AKI were included, and ten clusters were identified. Baseline creatinine differed significantly among clusters (p<0.001), highest in cluster 6 (2.90 ± 2.62 mg/dL) and lowest in cluster 2 (1.43 ± 1.10 mg/dL). Nephrotoxic medication use was highest in cluster 7 (46.8%) and lowest in cluster 8 (15.1%). Sepsis incidence was highest in cluster 5 (85.4%) and lowest in cluster 1 (14.1%). Regarding creatinine trajectories, clusters 5 and 7 exhibited abrupt elevations, while other clusters showed gradual increases. Clinical outcomes varied significantly across clusters (all p<0.001). The 7-day mortality rate was highest in cluster 3 (2.44%), followed by cluster 6 (0.53%), while the 7-day KRT initiation rate was highest in cluster 7 (25.00%), followed by cluster 8 (3.45%). Deep learning-based clustering identified clinically distinct AKI subtypes, which may facilitate future AKI risk stratification, biomarker discovery, and mechanistic research.