Being diagnosed with breast cancer (BC) can be a traumatic experience for patients who may experience symptoms of depression. In order to facilitate the prevention of such symptoms, it is crucial to understand how and why depressive symptoms emerge and evolve for each individual, from diagnosis through treatment and recovery. In the present work, data from a multicentric study of 706 BC patients followed for 12 months are analyzed. First, a trajectory-based unsupervised clustering based on K-means is performed to capture the dynamic patterns of change in patients' depressive symptoms after BC diagnosis and to identify distinct trajectory clusters. Then a supervised learning approach was employed to build a classification model of depression progression and to identify potential predictors. Patients were clustered into 4 groups: stable low, stable high, improving, and worsening depressive symptoms. In a nested cross-validation pipeline, the performance of the Support Vector Machine model for discriminating between "good" and "poor" progression was 0.78±0.05 in terms of AUC. Several psychological variables emerged as highly predictive of the evolution of depressive symptoms with the most important ones being negative affectivity and anxious preoccupation. Clinical Relevance-The findings of the present study may help clinicians tailor individualized psychological interventions aiming at alleviating the burden of these symptoms in women with breast cancer and improving their overall well-being.