Cardiovascular disease represents a significant global health challenge and the necessity for advanced techniques for early detection, diagnosis and management. This study explores Artificial Intelligence (AI) techniques in cardiovascular health analysis and decision-making processes. For instance, for patients experiencing Ventricular Ectopic Beats (VEBs), AI can recommend stress reduction and regular exercise. Using artificial neural networks, ElectroCardioGram (ECG) signals can be analyzed to detect abnormalities in various cardiovascular diseases. The proposed AI system includes a soft voting ensemble transfer learning method to process ECG data to classify different types of abnormalities in the heart, providing accurate and timely diagnostic support. Additionally, the system incorporates patient data to offer personalized treatment recommendations. Through extensive training and testing on a publicly available diverse dataset, the AI model demonstrates high accuracy and robustness in identifying critical cardiovascular conditions and decision making. This research underscores the potential for AI to revolutionize cardiovascular healthcare by enhancing diagnostic precision, facilitating early intervention, and ultimately improving patient outcomes. The implementation of such AI-driven solutions can significantly reduce the burden on healthcare systems and pave the way for more efficient and effective cardiovascular disease management. However, there are a number of issues with the medical application of AI techniques and applications and their findings and interpretations, such as confidential patient data, noisy data and biased data, which may lead to wrong conclusions. Still, AI is a next-generation technology that has significant potential in the medical field.