Artificial intelligence (AI) is transforming the landscape of cardiometabolic disease prevention and management through multimodal AI approaches that integrate diverse data sources. This review highlights the current state of medical AI in cardiometabolic disease, emphasizing the potential of multimodal AI to enhance personalized prevention and treatment strategies. AI has evolved from supervised learning to self-supervised learning, enabling the analysis of complex, multimodal data in medicine. In cardiology, machine learning has been applied to diagnostic tests such as electrocardiograms (ECGs) and echocardiograms, leading to improved diagnosis and prediction of adverse outcomes. Recent advancements in transformer models have enabled the analysis of large-scale, longitudinal datasets, leading to more accurate risk assessment and prediction. Multimodal AI has shown promise in improving risk assessment for cardiometabolic diseases by integrating data from various sources, including blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. AI models have been developed to predict cardiovascular disease more accurately, with some models achieving an AUC of 0.95 for CAD risk prediction. Additionally, AI has been used to improve blood pressure monitoring and management, with multimodal approaches incorporating biochemical, dietary, genomic, and metabolic inputs to enhance risk prediction. Sleep optimization is another area where AI has shown potential, with multimodal models improving the diagnosis of sleep disorders and predicting cardiovascular risk. AI has also been applied to stress and depression, with models incorporating data from wearable sensors and multimodal inputs to improve diagnosis and quantification. Physical activity and personalized exercise coaching have also benefited from AI, with models using wearable data to track and predict physical activity levels. In diabetes and glucose control, AI has been used to improve diagnosis and treatment strategies, with models predicting diabetes risk and identifying individuals at high risk. AI has also been used to improve precision nutrition, with models integrating data from the gut microbiome and metabolomics to personalize dietary recommendations. Despite the promise of AI in cardiometabolic disease, challenges remain, including data quality, algorithmic bias, and data privacy. However, the integration of multimodal AI into clinical practice holds significant potential for improving patient outcomes and personalized care.Artificial intelligence (AI) is transforming the landscape of cardiometabolic disease prevention and management through multimodal AI approaches that integrate diverse data sources. This review highlights the current state of medical AI in cardiometabolic disease, emphasizing the potential of multimodal AI to enhance personalized prevention and treatment strategies. AI has evolved from supervised learning to self-supervised learning, enabling the analysis of complex, multimodal data in medicine. In cardiology, machine learning has been applied to diagnostic tests such as electrocardiograms (ECGs) and echocardiograms, leading to improved diagnosis and prediction of adverse outcomes. Recent advancements in transformer models have enabled the analysis of large-scale, longitudinal datasets, leading to more accurate risk assessment and prediction. Multimodal AI has shown promise in improving risk assessment for cardiometabolic diseases by integrating data from various sources, including blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. AI models have been developed to predict cardiovascular disease more accurately, with some models achieving an AUC of 0.95 for CAD risk prediction. Additionally, AI has been used to improve blood pressure monitoring and management, with multimodal approaches incorporating biochemical, dietary, genomic, and metabolic inputs to enhance risk prediction. Sleep optimization is another area where AI has shown potential, with multimodal models improving the diagnosis of sleep disorders and predicting cardiovascular risk. AI has also been applied to stress and depression, with models incorporating data from wearable sensors and multimodal inputs to improve diagnosis and quantification. Physical activity and personalized exercise coaching have also benefited from AI, with models using wearable data to track and predict physical activity levels. In diabetes and glucose control, AI has been used to improve diagnosis and treatment strategies, with models predicting diabetes risk and identifying individuals at high risk. AI has also been used to improve precision nutrition, with models integrating data from the gut microbiome and metabolomics to personalize dietary recommendations. Despite the promise of AI in cardiometabolic disease, challenges remain, including data quality, algorithmic bias, and data privacy. However, the integration of multimodal AI into clinical practice holds significant potential for improving patient outcomes and personalized care.