Heart disease is a major cause of mortality worldwide, and stent placement is an effective treatment for many heart-related conditions. In this paper, we propose a novel robotic stent operation technique that uses an angiogram artery to identify and treat blocked areas in the heart. The images are real time images from an angiogram procedure. These images need to be obtained from the angiogram video, converting them into frames and images thus obtained are preprocessed [1–6]. The technique involves converting angiogram images into a tree data structure and using the shortest path algorithm to reach the blocked area. Motor-driven stent placement ensures precise and accurate placement, saving time and improving patient safety. Machine learning algorithms can enhance the identification of nodes and branches in the angiogram image, and predictive models can be developed to assess stent placement success. Additionally, incorporating shortest path algorithms into robotic-assisted stent placement can further improve the precision and efficiency of the procedure, ultimately leading to better patient outcomes.