The prediction of household food price index has always been a significant challenge for the food industry, especially in developing countries like India, where the majority of the population depends on agriculture for their livelihoods. In this project, we aim to develop a food price index prediction system for household food items like cereals, millets, and pulses using three popular time-series forecasting models, namely SARIMA, ETS, and FB Prophet. We use historical price index data to build and evaluate the forecasting models. The performance of each method is assessed using evaluation metrics such as MAE and RMSE. The results show that all three methods can effectively predict the demand for food items with high accuracy. However, FB Prophet has better performance than the other two methods when it comes to forecasting accuracy and computation time. This project presents a food prediction model that can be used by grocery stores and households to effectively plan and manage their food inventory. The study highlights the effectiveness of time series forecasting techniques such as SARIMA, ETS, and FB Prophet in predicting the demand for household food items, which can aid in reducing food wastage and improving food supply chain management The developed forecasting model can help retailers and suppliers to manage their inventory and plan their production based on the predicted demand for household food items. Additionally, this study provides valuable insights into the application of time series forecasting methods in the food industry.