This thesis examines the macroeconomic factors influencing electric vehicle (EV) adoption across a diverse set of developed and developing countries between 2010 and 2023. Using both linear and log-log regression models, it evaluates the effects of GDP per capita, FDI, urbanization, renewable energy use, CO₂ emissions, and government incentives on EV registration rates. The study finds that while linear models offer a basic understanding, log-log models provide more meaningful economic insights by capturing elasticities. Notably, the inclusion of government incentives significantly improved model performance, with the log-log model achieving an R² of 0.827. GDP per capita and renewable energy consumption emerged as the most consistent and significant predictors. In contrast, the impact of urbanization, CO₂ emissions, and policy incentives varied depending on country context. The research highlights a pronounced disparity between developed and developing nations in their readiness for EV adoption, driven by differences in institutional capacity and infrastructure. Overall, the study offers valuable insights for policymakers and underscores the importance of tailored strategies to support a sustainable and inclusive EV transition.