Electric vehicles (EVs) are increasingly promoted in many countries to reduce transportation-related greenhouse gas emissions, offering numerous environmental and economic benefits, such as lower tailpipe emissions and operating costs. However, predicting EV adoption entails access to comprehensive data sets. This study introduces a cost-effective alternative by leveraging open-source socioeconomic and demographic (SED) data from census records and machine learning (ML) models. Focusing on high-resolution geography at the dissemination area level, this study examines the association of SED characteristics, urbanization, annual vehicle kilometer traveled (VKT), and charging infrastructure to predict EV adoption. Ensemble ML models, particularly eXtreme Gradient Boosting, achieve superior predictive accuracy (up to 95%), with forward sequential feature selection identifying 18 key features that enhance model performance. Furthermore, Shapley Additive exPlanation analysis indicates that higher education, income, urbanization, and charging infrastructure availability are strong drivers of EV adoption. In contrast, high population density, longer VKT, and extended commuting durations pose barriers. This approach validates the existing determinants of EV uptake and introduces a scalable, reproducible framework for policymakers. This study demonstrates the feasibility of high-resolution spatial forecasting by leveraging publicly available data. In addition, it provides actionable insights to support targeted policies and infrastructure development to accelerate EV adoption.