Electoral violence remains a significant challenge worldwide. It not only threatens to undermine the legitimacy and fairness of electoral outcomes, but often has serious repercussions on political stability broadly. The ability to prevent electoral violence is critical for safeguarding democracy and ensuring peaceful transitions of political power. Predicting which elections are at risk of violence is a crucial step in effective prevention. In this study, we develop a set of machine-learning models to forecast the likelihood of electoral violence worldwide. Using diverse data sources, which include economic indicators, the history of electoral violence, political instability, and digital vulnerability, we predict the risk of electoral violence on a scale ranging from no violence to severe violence. Our final forecasts are produced by combining constituent models into an ensemble using a genetic algorithm. Out-of-sample evaluation of the system shows that the final model accurately distinguishes between different levels of risk. After validating our system on historical data, we generate out-of-sample probabilistic forecasts for national-level elections in 2025 and 2026. This research contributes to the field of political violence prediction by providing a medium-term data-driven forecasting tool for electoral violence.