<p>Cardiovascular signals such as photoplethysmography, electrocardiography and blood pressure are inherently correlated and complementary, together reflecting the health of the cardiovascular system. However, their joint utilization in real-time monitoring is severely limited by diverse acquisition challenges from noisy wearable recordings to burdened invasive procedures. Here we propose UniCardio, a multimodal diffusion transformer that reconstructs low-quality signals and synthesizes unrecorded signals in a unified generative framework. Its key innovations include a specialized model architecture to manage the signal modalities involved in generation tasks and a continual learning paradigm to incorporate varying modality combinations. By exploiting the complementary nature of cardiovascular signals, UniCardio clearly outperforms recent task-specific baselines in signal denoising, imputation and translation. The generated signals match the performance of ground-truth signals in detecting abnormal health conditions and estimating vital signs, even in unseen domains, as well as ensuring interpretability for human experts. These advantages establish UniCardio as a practical and robust framework for advancing artificial-intelligence-assisted healthcare.</p>