Machine learning algorithms are increasingly used for designing architectured materials with extreme properties. In this study, we employ the variational autoencoders (VAE) to generate microlattice structures with desired mechanical properties. We create unique datasets comprising 10,000 point cloud samples such as Kelvin foam and octet-truss by manipulating design parameters such as voxel size and strut thickness. The stiffness matrices for the structures are calculated using MATLAB. The datasets are split into training, validation, and test sets at a ratio of 8 : 1 : 1. During VAE training, a contrastive loss term ensures that similar properties cluster closely in the latent space, with the regressor predicting property. In the latent space, we generate new data by interpolating existing data with desired property. These new data are subjected to surface reconstruction to produce 3D meshes, which are further validated through 3D printing and mechanical testing. While our current framework relies on the Vanilla VAE and primarily focuses on the structural property, future work might explore advanced models, potentially broadening the property domains beyond mechanical properties to design multifunctional lattices tailored for varied applications.