The present research focuses on developing and optimizing path-planning strategies for an Autonomous Mobile Robot (AMR) for food delivery in restaurant environments. Utilizing the RPLiDAR, A1M8 360° sensor, and the computational power of the Raspberry-Pi 4B by designing SLAM algorithms that allow the robot to navigate dynamically in restaurant spaces, ensuring efficient delivery routes while considering safety margins, time efficiency, and energy consumption. The unique application of this robot lies in its purpose to serve as an autonomous food delivery system, seamlessly transporting dishes from the restaurant kitchen to customers’ tables. Integrating classical optimization techniques, heuristic approaches, and machine learning algorithms, the research addresses the specific challenges of restaurant navigation and aims to enhance the adaptability and autonomy of the robot. Rigorous testing in simulated and real-world restaurant scenarios will validate the algorithm’s performance, contributing valuable insights to autonomous robotic systems in the context of restaurant service applications.