Cardiovascular diseases (CVDs) remain the leading cause of global mortality, necessitating early and precise diagnostic approaches. Traditional diagnostic techniques often rely on unimodal data sources, leading to fragmented and sometimes inaccurate assessments. The advent of machine learning (ML) and deep learning, particularly Vision Transformers (ViTs), has revolutionized medical diagnostics by enabling a multimodal fusion approach. This paper explores the integration of medical imaging, structured clinical data, and patient history to enhance CVD detection accuracy. By leveraging ViTs for image analysis and ML techniques such as random forest and gradient boosting for structured data, a holistic and data-driven approach to early CVD diagnosis is proposed. The study underscores the benefits of multimodal fusion in capturing complex patterns, reducing diagnostic errors, and enabling precision medicine. Ultimately, this methodology paves the way for more efficient, scalable, and accessible diagnostic tools in both well-resourced and under-resourced healthcare environments.