This review summarizes recent advancements in the application of artificial intelligence (AI) in multi-modal imaging for cardiovascular disease. The paper highlights the integration of diverse imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), echocardiography, and x-rays, along with electronic health records (EHR), to improve diagnostic accuracy and clinical decision-making. The authors discuss challenges in integrating these modalities, including the limited number of studies addressing non-imaging modalities and the lack of real-world clinical validation. The review covers key areas such as image registration, segmentation, and fusion, which are essential for aligning and combining data from different sources. Several studies are highlighted, including models that use deep learning for segmentation and fusion of MRI and CT scans, as well as methods for predicting cardiovascular disease risk using multimodal data. The paper also discusses the potential of AI in improving diagnostic accuracy and reducing healthcare costs. Despite progress, there are still significant challenges, including the need for more diverse datasets, better integration of non-imaging modalities, and validation in real-world clinical settings. The authors emphasize the importance of further research to address these limitations and to develop more robust and interpretable AI models for cardiovascular imaging. Overall, the integration of AI with multi-modal imaging holds great promise for improving the diagnosis and treatment of cardiovascular diseases.This review summarizes recent advancements in the application of artificial intelligence (AI) in multi-modal imaging for cardiovascular disease. The paper highlights the integration of diverse imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), echocardiography, and x-rays, along with electronic health records (EHR), to improve diagnostic accuracy and clinical decision-making. The authors discuss challenges in integrating these modalities, including the limited number of studies addressing non-imaging modalities and the lack of real-world clinical validation. The review covers key areas such as image registration, segmentation, and fusion, which are essential for aligning and combining data from different sources. Several studies are highlighted, including models that use deep learning for segmentation and fusion of MRI and CT scans, as well as methods for predicting cardiovascular disease risk using multimodal data. The paper also discusses the potential of AI in improving diagnostic accuracy and reducing healthcare costs. Despite progress, there are still significant challenges, including the need for more diverse datasets, better integration of non-imaging modalities, and validation in real-world clinical settings. The authors emphasize the importance of further research to address these limitations and to develop more robust and interpretable AI models for cardiovascular imaging. Overall, the integration of AI with multi-modal imaging holds great promise for improving the diagnosis and treatment of cardiovascular diseases.