The colorization of sketches is a key aspect in computer vision, with applications ranging across the art, design and entertainment realms. True conversion of a monochromatic sketch into color is an invaluable skill for creative people and enthusiasts. Machine learning techniques, especially deep learning, have shown significantly better performance in various computer vision tasks, particularly for image segmentation, object classification, and other tasks. Although these methods are very promising, the literature still lacks concrete examples of GAN+autoencoder techniques and an in-depth discussion of their integration. Our research adopts a novel method, which integrates GANs and autoencoders to fill this gap. The intent of this integration is to improve the accuracy and effectiveness of color images generated from sketches. But our tests show that the proposed model is superior to existing techniques in terms of capturing fine details and creating a pleasing image. These results have significance in that they represent a breakthrough for state-of-the art sketch colorization techniques, and enable artists and designers to create realistic images of their sketches. This research not only highlights the lack of integration among GANs and autoencoders; it also offers an entirely new approach that helps both to improve the accuracy and degree of realism in colorized images. This represents a major breakthrough for sketch colorization techniques overall.