In the field of materials science, the analysis and classification of crystal structures play a crucial role in understanding the properties and behaviour of materials. Traditional optimization algorithms have limitations in effectively handling the complex and multi-objective nature of this task. Therefore, there is a need for a more advanced and efficient optimization technique to address this challenge. This study focuses on the potential of adopting Multi-Objective Grey Wolf Optimization (MOGWO) for the effective analysis and classification of crystal structures in materials science. The methodology of this review involves a comprehensive analysis of existing literature on the application of MOGWO in materials science, particularly in the context of crystal structure analysis and classification. The review includes a critical evaluation of the strengths and limitations of MOGWO compared to traditional optimization algorithms. Additionally, classification, minimization, and optimization of crystal structures as well as structural stability, electrical properties, inter-atomic distance, lattice parameters, and phase transformation via MOGWO were also examined. The findings revealed that MOGWO offers several advantages over traditional optimization algorithms in the analysis and classification of crystal structures. For example, MOGWO is able to effectively handle the multi-objective nature of material crystal structures, providing more accurate and efficient results. Furthermore, MOGWO has been shown to outperform other optimization techniques in terms of convergence speed, improved accuracy, efficiency, and solution quality. The algorithm can effectively handle the complexity and multi-objective nature of problems in this field, providing more accurate results within the least possible time. This study highlights the potential of MOGWO as a valuable research tool in the field of materials science, offering a more efficient and effective approach that can advance our understanding of crystals.