Coronary artery disease, a prevalent condition often leading to heart attacks, may cause abnormal wall shear stresses near stenosed regions generating high frequent acoustic shear waves. In a previous study, a viscoelastic agarose gel was used to model the human tissue and it was shown that two material parameters of the gel could be estimated with a high certainty using a classical inverse problem. Given the high computational cost of traditional methods, this paper explores machine learning (ML) alternatives, particularly a Variational Autoencoder Inverse Mapper (VAIM). VAIM, previously successful in nuclear physics, uses neural networks to approximate forward and backward mappings and learn posterior parameter distributions. This paper validates previous research by generating data around ground truth values, demonstrating VAIM's ability to estimate two material parameters effectively. Further, it addresses realistic applications by training and testing on noisy data and generalizing findings across different intervals of signal damping.