SLAM system that enables an autonomous robot to efficiently navigate and map a wide range of locations. Numerous industries, such as driverless vehicles, warehouse logistics, search and rescue operations, industrial automation, and more uses the suggested SLAM-based robot. The present research work tackles the vital need for precise mapping and self-localization in dynamic environments by seamlessly merging sensor data with cutting-edge algorithms, thereby contributing to the growth of automation and robotics in a variety of industries. This work demonstrate the seamless integration of Robot Operating System (ROS) with a suite of hardware components, including LiDAR (Light Detection and Ranging), IR (Infrared) sensors, motors, motor drivers, and Raspberry Pi to create a comprehensive autonomous robotics platform. The ROS framework serves as the central nervous system, enabling real-time data processing, sensor fusion, and motor control for diverse applications such as navigation, obstacle avoidance, and environmental mapping. The findings of this study provide a summary of the essential elements and their functions in creating a flexible and competent autonomous robot, showing the potential for developments in areas like robotics, automation, and AI-driven systems.