Borderline personality disorder (BPD) is a severe and chronic psychiatric disorder that has been defined by the presence of characteristic symptoms, including impulsivity, affected instability, identity disturbance with significant functional impairment, and early trauma. This entry seeks to consider the incorporation of computational approaches in decoding, diagnosing, and treating BPD to build an argument that it can facilitate participatory health with higher precision, personalization, and accessibility. By integrating multimodal data sources (including ecological momentary assessment, digital phenotyping, neuroimaging, and social media analytics), computational psychiatry uses machine learning, dynamic systems, and reinforcement learning models to identify the correlates of emotional dysregulation, maladaptive decision-making, and symptom fluctuations in BPD. This entry also covers ethical concerns, disability-inclusive design, and issues of data quality, interpretability, and privacy. It further describes use-case studies of applications such as adaptive digital therapeutics, personalized intervention planning, and hybrid human–AI decision-making systems. Future directions emphasize the potential of culturally humble, intersectional, and patient-centered computational models for improving BPD treatment globally.