The disease management of long‐term breast cancer survivors (BCS) is hampered by the scarce knowledge of multimorbidity patterns. The aim of our study was to identify multimorbidity clusters among long‐term BCS and assess their impact on mortality and health services use. We conducted a retrospective study using electronic health records of 6512 BCS from Spain surviving at least 5 years. Hierarchical cluster analysis was used to identify groups of similar patients based on their chronic diagnoses, which were assessed using the Clinical Classifications Software. As a result, multimorbidity clusters were obtained, clinically defined and named according to the comorbidities with higher observed/expected prevalence ratios. Multivariable Cox and negative binomial regression models were fitted to estimate overall mortality risk and probability of contacting health services according to the clusters identified. 83.7% of BCS presented multimorbidity, essential hypertension (34.5%) and obesity and other metabolic disorders (27.4%) being the most prevalent chronic diseases at the beginning of follow‐up. Five multimorbidity clusters were identified: C1‐unspecific (29.9%), C2‐metabolic and neurodegenerative (28.3%), C3‐anxiety and fractures (9.7%), C4‐musculoskeletal and cardiovascular (9.6%) and C5‐thyroid disorders (5.3%). All clusters except C5‐thyroid disorders were associated with higher mortality compared to BCS without comorbidities. The risk of mortality in C4 was increased by 64% (adjusted hazard ratio 1.64, 95% confidence interval 1.52‐2.07). Stratified analysis showed an increased risk of death among BCS with 5 to 10 years of survival in all clusters. These results help to identify subgroups of long‐term BCS with specific needs and mortality risks and to guide BCS clinical practice regarding multimorbidity.