Parallel Multi-Objective Evolutionary Algorithms (pMOEAs) are powerful search techniques used to solve complex problems across various fields. This study presents a comprehensive review of the current state of pMOEAs, analyzing their effectiveness, benefits, limitations, and comparisons with traditional Multi-Objective Evolutionary Algorithms (MOEAs). The methodology involved a systematic review of the literature using academic databases such as ACM Digital Library, ScienceDirect, Springer, Scopus, Google Scholar, Web of Science, and IEEE Xplore. Findings indicate that pMOEAs offer significant improvements in performance and robustness compared to traditional MOEAs. Parallelization enables tasks to be divided into subtasks that are solved simultaneously, enhancing efficiency and reducing execution time. Several parallel approaches are discussed, including master-slave, island, diffusion, and hybrid models. The study concludes that pMOEAs provide substantial advancements in performance and robustness over methods like NSGA-II and SPEA2, facilitating more efficient optimization of complex problems through parallel computing. Nonetheless, challenges remain in areas such as scalability, load balancing, and computational efficiency, underscoring the need for hybrid methodologies and advancements in these aspects to address increasingly complex problems.