As technology nodes advance and feature sizes shrink, the increasing complexity of design rules and routing congestion has resulted in greater design challenges and rising costs. Machine learning (ML) models offer significant potential to enhance design quality by enabling early prediction and optimization during the design flow. However, only a few works have validated the effectiveness of ML model when integrated to the traditional design flow. This paper will cover the effectiveness of ML-enhanced design workflow with some practical applications. Additionally, we will address which problems should be solved to achieve successful ML integration.