Modern vehicle manufacturing faces increasing challenges due to the complexity of wiring harness design for diverse variants, each with unique requirements and configurations. This paper introduces a transformative AI-driven approach to address these complexities. By utilizing Convolutional and Deep Neural Networks (CNN & DNN), the proposed system automates the generation of harness routing based on predefined specifications and constraints, significantly reducing human intervention, design time, and complexity. AI-trained models can also predict failures in production, minimizing downtime and improving efficiency, while AI integration seamlessly translates design parameters into manufacturable outcomes, optimizing tasks such as connector placements, grommet fittings and clip alignments. To tackle the inherent dynamic variant complexity, the system employs a novel framework that learns and adapts to unique design constraints in real-time. Unlike static master-slave frameworks, the dynamic AI system processes input parameters such as routing through the shortest spline path within available spaces and autonomously crafts a harness network optimized for feasibility and manufacturability. This approach allows for intelligent design scalability across truck variants through a unified algorithm. Upon order placement, the system dynamically integrates specific requirements, automating the design process and ensuring optimal performance. By eliminating manual intervention and adapting to diverse configurations, the AI system revolutionizes the wiring harness design workflow, enhancing production scalability, improving reliability, and reducing downtime across the manufacturing process. This paper highlights a novel AI-powered solution to streamline variant complexity, offering a scalable and efficient approach to modern truck harness design and manufacturing.