Recent advancements in computational techniques have significantly improved the capability to obtain optimal solutions for arduous engineering design problems, particularly those involving multiple intricate objectives. The challenge of simultaneously addressing competing objectives while adhering to various design constraints has become central in many engineering implementations. This study explores the implementation of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) framework to solve constrained multi-objective arduous engineering design problems. While the algorithmic performance of NSGA-III has been extensively evaluated in standard optimization settings, its applicability and effectiveness in real-world practical constrained multi-objective engineering design scenarios remain relatively underexplored. The NSGA-III utilizes the reference points concept to identify the best trade-offs between competing objectives, thereby enhancing decision-making in engineering design. To assess the robustness and accuracy of the algorithm, five well-established multi-objective arduous engineering design problems featuring mixed design constraints are investigated. These design problems, which were previously addressed using the NSGA-II, are now optimally solved using NSGA-III, allowing for a direct comparison between the two approaches. The key distinction of NSGA-III lies in its ability to efficiently handle multi-objective optimization problems, offering a superior solution quality and diversity compared to NSGA-II, especially when dealing with complex constraint-driven objectives. A comparison of the results with NSGA-II demonstrates the superior performance, stability, and reliability of the NSGA-III approach. The findings highlight the effectiveness of NSGA-III in providing high-quality, robust solutions to constrained multi-objective arduous engineering design problems with diverse design constraints.