Cardiovascular diseases (CVDs) are a leading cause of mortality globally. In this study, we propose a novel transfer learning model for the early detection of CVDs utilizing iterative autoregressive processes. By leveraging pre-existing knowledge from related tasks, our model achieves substantial improvements in predictive performance compared to conventional methods. Our approach takes into account temporal data patterns and utilizes a sophisticated autoregressive technique to extract meaningful features from raw medical data, enhancing the model's capability to detect early indicators of CVDs. The transfer learning framework further amplifies the model's efficiency by reusing knowledge from related tasks, minimizing the need for extensive new data samples. Extensive evaluations were conducted using various benchmark datasets, demonstrating the model's superior performance in CVD detection. The proposed approach shows promising potential in enhancing the early detection of CVDs, enabling timely interventions and reducing the overall burden of these diseases.