<p>The effects of climate change and the need for sustainable practices have brought attention to the need to handle end-of-life (EOL) item disposal, especially in sectors like automotive manufacturing. The idea of a circular economy, which encourages repairing, reusing, and recycling goods, has gained popularity as an achievable option. The circular economy is greatly aided by remanufacturing, which prolongs the life of EOL products. Robotic disassembly is a crucial step in the remanufacturing process, but it is difficult because EOL products vary so much and because industrial robots nowadays have certain limits. Therefore, the research will focus on the creation of a 3D spatial model and two heuristic branches of artificial intelligence (AI): a Bee Swarm Optimization (BSO) algorithm and a Genetic Algorithm (GA) that utilizes prior spatial information for automated disassembly path planning. The efficiency of the automated disassembly path planning will be evaluated using two evaluation techniques: Chebyshev and the Euclidean distance, to minimize the path for Disassembly Path process. The results for the extraction of spatial information using slicing technology were satisfactory, however, the BSO and GA results can be enhanced more by introducing complex optimization techniques. The necessity to use AI and CAD models to optimize the disassembly process while considering the limitations and complexities of EOL products and industrial robots forms the basis for this research. The focus of this paper is on automated robotic disassembly planning, including planning the disassembly sequence and identifying spatial restrictions. The design of a robotic environment or of custom algorithms is not part of the research.</p> Graphical Abstract <p></p>