With the rapid development of Generative Adversarial Networks (GANs), facial sketch generation and age transformation have advanced considerably. These technologies show great potential in digital media, entertainment, and forensic applications, particularly in helping law enforcement reconstruct the appearance of long-term fugitives. However, current methodologies exhibit notable limitations: existing approaches typically specialize in either facial sketch generation or age progression independently, lacking an effective integration for cross-domain synthesis. Moreover, preserving identity information while ensuring high-quality image generation remains a challenge. This paper proposes Multi-Scale Feature Extraction Networks (MSFE), an image-to-image translation framework that enables continuous age transformation while maintaining the stylistic characteristics of sketch domains. The core of the MSFS network uses a Dual Conditional Normalization Attention (DCNA) architecture to extract sketch features and encode facial images into the latent space of a pre-trained StyleGAN based on the desired age change. Experimental results on public datasets demonstrate that our approach outperforms existing methods, achieving superior facial photo-sketch synthesis with enhanced realism, identity preservation, and age accuracy.