This paper presents a machine learning based modeling methodology to analyze the impact of high-volume manufacturing process variations on electrical performance of high-speed interconnects, that overcomes the limitations of traditional approaches. The proposed methodology outperforms the response surface based modeling for high-speed interconnects and is capable of handling highly nonlinear relationships. Machine learning is demonstrated to be a promising approach to explore design spaces efficiently and accurately even when modeling data is limited due to expensive computational cost.