This study explores the application of the Autoregressive Integrated Moving Average (ARIMA) model to forecast prices of key agricultural commodities: rice, wheat, cotton, maize,and groundnuts. Effective price forecasting in agriculture is crucial for stakeholders includingfarmers, traders, and policymakers to make informed decisions. The ARIMA model is chosenfor its capability to capture time series patterns and fluctuations in commodity prices, which are influenced by diverse factors such as weather conditions, market demand, and governmentpolicies. Historical price data spanning a significant period is utilized to train and validate themodels for each commodity. The accuracy of ARIMA forecasts is evaluated using statistical metrics, and the results are compared across commodities to identify patterns and trends. The findings of this study contribute to enhancing the understanding of price dynamics in agricultural markets and provide valuable insights for mitigating risks and optimizing decision-making processes in the agriculture sector.