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Cardiovascular disease remains a leading cause of premature mortality and disability globally, necessitating early and effective diagnostic imaging strategies to enhance population health and quality of life. This study explores the utilization of various cardiovascular imaging modalities--including echocardiography, nuclear imaging, computed tomography (CT), magnetic resonance imaging (MRI), and invasive catheterization--as preventative measures to identify cardiovascular issues before they escalate. Each imaging technique offers unique advantages, from the noninvasive, high-resolution capabilities of echocardiography to the detailed anatomical insights provided by cardiac MRI and the real-time analysis possible with catheterization. However, the limitations and associated risks, such as exposure to radiation and contrast-induced nephropathy, must be carefully considered. Furthermore, the integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) into cardiovascular imaging is emerging as a transformative frontier, enabling enhanced prognostication and diagnostic accuracy. This article underscores the importance of adopting multimodal imaging approaches, emphasizing the need for ongoing research to improve imaging protocols and the application of AI technologies to optimize patient outcomes and streamline clinical decision-making in cardiovascular care. [ABSTRACT FROM AUTHOR]