This research describes the application of a Vector Autoregression (VAR) model to forecast food prices in Ukraine. The analysis covers the period from January 2014 to December 2021 and focuses on wheat, corn, sunflower, pork, gasoline prices, and European wheat prices. The analysis identified a positive trend in prices for wheat, corn, sunflower, and pork over the studied period. A VAR model was built using price data with three lags to capture interrelationships between the variables. The model achieved high determination coefficients (0.93–0.98) indicating a good fit between the model and the data. The model forecasts a downward trend for wheat prices with limited volatility. The model excludes external factors such as the 2022 war and its geopolitical consequences, potentially leading to inaccurate predictions. The VAR model provides valuable insights into historical food price trends in Ukraine. However, its limitations in handling non-linearities and unforeseen events make it less suitable for forecasting during periods of high market uncertainty, such as wartime. Such a food price forecasting system could provide more accurate short-term forecasts, allowing policymakers to take proactive measures. For instance, if a price spike is predicted for a specific commodity, targeted subsidies or import agreements could be implemented to mitigate food insecurity. By incorporating dynamic food price forecasting into resilience-building strategies, Ukraine can be better prepared to navigate market uncertainties and safeguard its food security.