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ABSTRACT Aim Cognitive dysfunction is a transdiagnostic feature of mood disorders and a key determinant of functional outcomes. This study aimed to identify transdiagnostic cognitive heterogeneity among patients with mood disorders using a data‐driven approach and to examine its association with social functioning. Characterizing cognitive subtypes and their association with subjective functional disability is an essential first step toward stratified interventions and personalized care. Methods We analyzed data from 264 age‐ and sex‐matched patients (132 each with major depressive disorder [MDD] or bipolar disorder [BD]). Participants completed six neurocognitive tests to assess executive function, memory, attention, and processing speed. Cognitive variables were adjusted for age and sex, and unsupervised K‐means clustering was used to identify transdiagnostic subgroups. Functional impairment was evaluated using the Sheehan Disability Scale. Results After matching participants based on age and sex, we compared the cognitive performance between the diagnostic groups. Significant differences were observed in only two cognitive variables with a small effect size (mean |Cohen's d | = 0.14). No differences were found in the social functioning measures. Cluster analysis identified two cognitive subtypes: higher‐performance group ( n = 200) and lower‐performance group ( n = 64) with a large effect size (mean |Cohen's d | = 0.99). Both clusters included patients with MDD and BD in similar proportions ( p = 0.11), indicating a diagnostic overlap. The lower‐performance group also showed significantly greater work‐related and total functional impairments. Conclusion Unsupervised clustering revealed transdiagnostic cognitive subgroups in mood disorders independent of diagnostic labels. Cognitive phenotyping may inform personalized treatment strategies and improve functional outcomes by identifying high‐risk patients for cognitive remediation or neuromodulation therapies.