Metamaterials are subwavelength-sized artificial structures with the ability to manipulate incident waves in such a way that affects how the energy propagates throughout the medium. In acoustics, particularly placed scattering elements can reduce the total scattering cross section (TSCS) response. We propose a method to inversely design acoustic metamaterial configurations using deep learning and generative modeling. Using our proprietary multiple scattering solver with MATLAB optimization toolbox, we generate a dataset of optimal configurations with minimized TSCS within a discrete range of wavenumbers. We use this dataset to train a Conditional Wasserstein Generative Adversarial Network (cWGAN) to generate similar metacluster designs corresponding to specified input TSCS. To improve the coordinate recognition ability of the cWGAN, we include the novel CoordConv layer in the generator and critic. After training, the cWGAN can produce a variety of optimal configurations given an expected TSCS. Evaluating TSCS of generated configurations shows that the model is capable of proposing scatterer configurations that are comparable or better than the dataset within the optimized range.