Electric vehicles (EVs) are reshaping the automotive industry, offering a sustainable alternative to fossil fuel-powered transportation. China and the United States, as global leaders, have accelerated EV adoption through investments and supportive policies to transition to clean energy and reduce greenhouse gas emissions. However, challenges such as raw material supplies, limited charging infrastructure, material recycling costs, and trade restrictions require greater social awareness and governmental intervention to improve affordability and expand consumer access. This study applies machine learning techniques to analyze 20 key economic, social, and environmental factors influencing EV sales in China and the U.S. from 2008 to 2024. The objective is to identify critical indicators that policymakers should prioritize to sustain industry growth. Findings indicate that in the U.S., key drivers include EV battery demand, EV-related jobs, the Consumer Price Index, charging infrastructure, interest rates, household income, and battery costs. In China, additional factors such as GDP, education levels, inflation rates, and exchange rates play a significant role. Ridge Regression achieves an accuracy of 92% with a mean squared error of 0.07, demonstrating strong predictive performance with minimal computational time. These insights are essential for policymakers, manufacturers, and stakeholders to develop effective strategies that drive EV market expansion, sustainability, and innovation.