This paper presents a novel approach to automated robot programming and robot integration in manufacturing domain and minimizing the dependency on manual online/offline programming. Traditional industrial robots programming is typically done by online programing via teach pendants or by offline programming tools. This presents a major challenge as it requires skilled professionals and is a time-consuming process. In today’s competitive market, factories need to harness their full potential through smart and adaptive thinking to keep pace with evolving technology, customer demand, and manufacturing processes. This requires ability to manufacture multiple products on the same production line, minimum time for changeovers and implement robotic automation for efficiency enhancement. But each custom automation piece also demands significant human efforts for development and maintenance. By integrating the Robot Operating System (ROS) with vision-based 3D model generation systems, we address these challenges effectively. A ROS-based framework has been developed to automate the manual offline robot programming and enable real-time task optimization for performing manufacturing operations such as painting, welding, and torquing. The proposed framework—Capture → Connect → Compile → Create—using RGBD camera systems to record 3D point cloud data and part details. It then connects the complete points, annotate features, interprets edges and tasks to be performed and then convert into executable robotic programs. This method significantly reduces manual programming efforts and enables rapid deployment of robotic systems across diverse tasks. The paper outlines the system architecture, implementation methodology, and integration strategy within existing manufacturing lines. Through autonomous robotic programming, this approach enhances mass customization and boosts overall manufacturing efficiency. The proposed system offers a scalable solution for smart factories aiming to achieve high productivity, flexibility, and reduced operational costs.