Generation of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to produce high-quality 3D artistic images. To produce 3D artistic images from 2D image that considerably improves scalability and visual quality, this research integrates innovative variational autoencoder signed distance function, cycle generative adversarial network (VAE-SDFCycleGAN). The proposed method feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into latent representations, and implicit surface representations of 3D images corresponding to those of 2D images are subsequently generated. VAE extracts feature from the two-dimensional input image and reconstructs a voxel-type grid using a signed distance function. Cycle GAN produces improved and high-quality 3D artistic images from 2D images. The publicly available COCO dataset was used to evaluate the proposed advanced 3D-VAE-SDFCycleGAN. The model produced a peak signal noise ratio (PSNR) of 31.35, mean square error (MSE) of 65.32, and structural similarity index measure (SSIM) of 0.772 which indicates the improved quality of the generated images. The results are compared with other traditional GAN methods and the results obtained show that the proposed method outperforms the others in terms of quantitative and qualitative evaluation metrics.