This research investigates the transformative potential of combining entrepreneurial ecosystems and 3D deep learning techniques in the realm of cardiac care within the field of medical technology. Specifically, we focus on the enhancement of heart imaging using the YOLOv5 deep learning model, with the aim of improving diagnostic accuracy. Leveraging open-source and 3D Cardiac MRI datasets, our study delves into how entrepreneurial ecosystems can expedite the development and implementation of advanced technologies like 3D deep learning in cardiac imaging. We introduce an interactive application, the "AI-powered 3D Cardiac Imaging App," developed using the Streamlit framework, which demonstrates remarkable accuracy of approximately 96.4%. This advancement highlights the potential for innovation within the MedTech sector when entrepreneurial ecosystems encourage the integration of AI methodologies. It not only attracts investments but also fosters skill development. Furthermore, our research sheds light on the influence of such technological advancements on policymaking, emphasizing the need for robust support for entrepreneurial ecosystems to foster future medical technology innovations. While this study underscores the transformative impact of AI integration in healthcare, it also underscores the necessity for more comprehensive studies, cross-disciplinary collaborations, and adaptive policymaking to keep pace with rapid technological developments. By integrating 3D deep learning techniques within entrepreneurial ecosystems, this research illuminates a path to revolutionize cardiac care, offering practical insights for medical technology entrepreneurs, healthcare professionals, and policymakers to guide their decision-making processes.