Topology optimization is a branch of structural optimization which solves an optimal material distribution problem. The resulting structural topology, for a given set of boundary conditions and constraints, has an optimal performance (e.g. minimum compliance). Conventional 3D topology optimization algorithms achieve quality optimized results; however, it is an extremely computationally intensive task which is, in general, impractical and computationally unachievable for real-world structural optimal design processes. Therefore, the current development of rapid topology optimization technology is experiencing a major drawback. To address the issues, a new approach is presented to utilize the powerful abilities of large deep learning models to replicate this design process for 3D structures. Adversarial models, primarily Wasserstein Generative Adversarial Networks (WGAN), are constructed which consist of 2 deep convolutional neural networks (CNN) namely, a discriminator and a generator. A minimax game is conducted between the generator and the discriminator as part of training where the discriminator maximizes the loss function whereas the generator tries to minimize the loss function of the model. Once trained, the generator from GAN can produce 3D structures in a computationally inexpensive process instantaneously. The corresponding input variables of the new generated structures are evaluated using a trained convolutional neural network. The dataset needed for training is generated using the traditional 3D topology optimization algorithms. Results from the GANs are validated by comparing these optimal structures against the 3D structures generated from the traditional algorithms with the same design settings. The potential issues and future extension of this work are discussed in detail in the article. As illustrated, introducing deep learning into the field of design will remarkably reduce the work time of an iterative design process.