This study aims to explore the convergence between an AI-Powered Retina-Based Diagnostic Tool and state-of-the-art deep learning models that can pave the way for early identification of the risk of cardiovascular disease (CVD). A critical review was conducted using 30 studies that demonstrated the impressive potential of deep learning methodologies with an ability to extract the risk factors pertaining to CVD from retinal images. These methodologies acquire tracking retinal-vessel characteristics with remarkable accuracy, revealing rich signs of CVD risk factors. The pioneering AI algorithm, born out of this research and precise calculation of retinal vasculature from retinal images, has leaped a giant step forward of fusing retinal vasculature imaging with traditional risk factors—a breakthrough prediction model for predicting cardiovascular events. Whether strategizing ways to standardize methodologies across studies or creatively merging AI-enabled retinal imaging with traditional risk through the integration of AI-enabled retinal imaging with traditional risk factor, the study underlines the accelerating pace at power of integrating of AI into clinical practice. According to the World Health Organization, 17.9 million people die of CVDs annually—an estimated 31% of all deaths worldwide. The medical and social costs of their widespread prevalence—and the increasingly longer lifespans of many people residing in advanced economies—underscore the need for new, more effective diagnostic strategies to manage these conditions and reduce their impact.