Agricultural price forecasting plays an important role in production planning, cost management, and decision-making in the agricultural sector. However, many previous studies have primarily focused on forecasting techniques without integrating Business Intelligence systems for data analysis and decision support. This study aimed to 1) analyze trends in animal feed corn prices in Thailand using historical data, 2) develop and compare the performance of time series forecasting models, and 3) develop a Business Intelligence dashboard to support data analysis and decision-making. The study used monthly animal feed corn price data from 2021 to 2025, comprising 60 monthly observations. The dataset was divided chronologically into training and testing sets at a ratio of 80:20. The forecasting models included Simple Linear Regression, Moving Average, Exponential Smoothing, and Holt–Winters Exponential Smoothing. The results showed that the Exponential Smoothing model (α = 0.95) achieved the highest forecasting accuracy with a MAPE of 2.473%, followed by Moving Average (3.249%) and Holt–Winters (3.875%), while Simple Linear Regression produced the highest forecasting error. In addition, an interactive dashboard was developed using Looker Studio to visualize actual price data and forecasting results. The findings indicate that integrating accurate forecasting models with Business Intelligence systems can effectively support agricultural data analysis and decision-making.