Abstract Chronic cardiac disease remains one of the predominant causes of morbidity and mortality worldwide. To prevent and manage chronic heart disease, this research investigates the groundbreaking potential of predictive analysis and Artificial Intelligence (AI) tools. The healthcare infrastructures can enhance patient care, resource utilization optimization, and risk determination by leveraging machine learning and data-driven intelligence. This work discusses challenges and ethical issues associated with deploying AI-driven predictive analytics in healthcare. The technology holds immense promise for better patient outcomes and lower healthcare expenses, thus a valuable line of research and development within the healthcare field. Information from large numbers of patients and sophisticated machine learning methods can be used to develop models that forecast disease progression and identify high-risk patients. The dataset of ECG (Electro Cardio Gram) images, which contains a total of 929 images with 743 training images and 186 testing images, is from where the data are gathered. The accuracy of 54.57% was obtained by the CNN model, the accuracy of 70.71% was achieved by the VGG16 model, and the accuracy of 61.75% was obtained by the VGG19 model. Chronic heart disease’s burden on patients and the health system can be greatly alleviated with early detection and specific therapies. It also refers to challenges and ethical dilemmas in the application of AI in healthcare, given the value of data protection and confidentiality.