Cardiovascular disease (CVD) is a major comorbidity in asthma and chronic obstructive pulmonary disease (COPD), yet the contribution of artificial intelligence (AI) and machine learning (ML) to CVD risk assessment and management in these conditions remains insufficiently characterized. This scoping review identified the main original full-text studies applying AI/ML to the overlap between CVD and asthma or COPD for prediction, phenotyping or clinical decision support. Among the eleven identified studies, only one specifically addressed asthma, developing ML-based CVD risk prediction models from electronic health records that achieved good short-term discrimination but lacked external validation. The remaining studies focused on COPD and CVD, employing supervised learning, deep-learning survival analysis, natural language processing, unsupervised clustering and AI-enabled clinical decision support. Across these investigations, COPD and related comorbidities consistently emerged as strong predictors of CVD events, mortality and adverse clinical trajectories. Unsupervised clustering revealed COPD-dominant heart failure phenotypes with particularly poor outcomes, while AI-derived risk models frequently provided superior discrimination and calibration compared with traditional statistical approaches. However, most studies were retrospective, largely reliant on structured data, limited in generalizability and rarely implemented in routine care. Overall, current evidence indicates substantial potential for AI/ML to enhance CVD risk stratification, phenotyping and management in COPD, whereas applications in asthma are strikingly scarce. These findings underscore a critical need for large-scale, prospectively evaluated and clinically integrated AI/ML strategies to improve detection, risk stratification and personalized management of CVD in patients with asthma or COPD.