Major depressive disorder (MDD) is a leading cause of disability worldwide, yet its diagnosis relies on clinical symptoms alone. Using machine learning applied to deeply phenotyped, medication-free participants with MDD, we identified two neuroanatomical dimensions. Dimension 2 (D2), compared to Dimension 1 (D1), was characterized by reductions in grey and white matter and was associated with limited treatment response to both antidepressant and placebo medications. Validation in UK Biobank general population cohort (n = 37,235) confirmed that D2 is characterized by reduced grey and white matter, alongside widespread cognitive impairments, adverse events in both adulthood and childhood, increased self-harm and suicide attempts, a pro-atherogenic lipid profile, and genetic associations with neurodegenerative traits. These findings suggest that D1 and D2 reflect distinct neurobiological mechanisms underlying MDD, with important implications for and treatment outcomes. External validation was demonstrated in a general population-based cohort that delineated mechanisms underlying heterogeneity in MDD, identifying potential biomarkers that could aid in personalising treatment approaches for this debilitating disorder.