This study looks into the introduction of AI-driven electrochromic materials and devices into nanofabrication methods for use in ML-integrated environments. When exposed to an electric field, electrochromic materials experience reversible changes in optical properties due to dynamic optical modulation. Because of developments in AI-assisted design, optimization, and fabrication, advanced electrochromic devices with improved performance are now conceivable. The incorporation of AI-optimized electrochromic materials into nanofabrication operations and their application in ML-integrated systems are described, as well as their synthesis and characterization. Several test datasets revealed that the AI-driven strategy improved OME, Response Times, CE, and EE. These findings validate the importance of applying AI algorithms to guide material design, optimize production, and enable real-time adaptation for greater optical modulation and energy efficiency.