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 The proposed method significantly reduces the time required to find optimal solutions by eliminating the need for time-consuming finite element calculations during the optimization process. Instead, all resource-intensive computations are handled during the network training phase. The study provides a comprehensive review of the existing literature on applying machine learning to topology optimization, enhancing its relevance to current research trends. Additionally, the practical example involving an elastic plate demonstrates the potential of this approach, showcasing its flexibility for different geometric and boundary condition problems. This research opens up promising avenues for future exploration while also addressing some of the unresolved challenges in this field.