Purpose of Review <p>This review explores how artificial intelligence (AI) and machine learning (ML) techniques, particularly unsupervised models, enhance understanding of multimorbidity (MM) in cardiovascular disease (CVD). The goal is to evaluate how these methods can uncover novel disease clusters, characterize longitudinal disease evolution, and identify synergistic risks beyond traditional comorbidity indices.</p> Recent Findings <p>Recent studies demonstrate that clustering, tensor factorization, and probabilistic modeling uncover clinically meaningful MM phenotypes with distinct outcomes. Sequence-based models map disease trajectories, revealing critical transition points for intervention. Network analyses quantify interactive risks, while explainable AI methods improve clinical trust and model interpretability.</p> Summary <p>AI and ML can transform multimorbidity CVD research and clinical care by revealing hidden multimorbidity structures and trajectories. These innovations provide deeper insight into disease interdependencies, enabling earlier, more personalized, and equitable prevention and care strategies in complex patient populations.</p>