This paper presents a comprehensive study on the optimization of plant layout using two prominent Meta-heuristic algorithms: CORELAP and ALDEP. The plant layout optimization problem, recognized as an NP-hard challenge, concentrates on reducing the overall material handling cost by strategically arranging facilities and departments within a factory. CORELAP and ALDEP have emerged as pivotal tools for addressing this complex issue. In this study, we conduct a comparative analysis of these two algorithms, applying them to a real-world case study involving a manufacturing plant. The findings reveal that both algorithms excel in optimizing the layout; however, CORELAP demonstrates superior performance in terms of computational efficiency and solution quality. Moreover, this study introduces an innovative approach that integrates Python programming with ALDEP to generate a multitude of alternative solutions. This hybrid methodology significantly enhances the ability to visualize and assess numerous layout possibilities. Through exhaustive experimentation, it becomes evident that this hybrid approach, when combined with ALDEP, yields exceptional results, surpassing the capabilities of CORELAP. In conclusion, the research underscores the vital role of optimization in modern, competitive markets. It emphasizes the significance of minimizing waste, enhancing productivity, and achieving operational excellence through the optimization of manufacturing processes, material handling, and plant layout design. The study provides valuable insights for both industry practitioners and researchers in the realm of plant layout optimization, highlighting the potential of hybrid solutions like the one presented here to drive efficiency and competitiveness in manufacturing operations.