Wheat production is of paramount importance in both India and China, as it is very essential in food security and the livelihood of millions of people. Accurate forecasting of wheat production is essential for ensuring stable food supplies and effective agricultural policies. The ARIMA technique applied to time series analysis to assess and forecast wheat production in India and China. The process of inquiry begins with collecting historical wheat production data from Kaggle dataset. The ARIMA-based models are used to provide interesting insights within the parameters and dynamics of wheat production in India and China. The models effectively capture the seasonality and trend components of historical data, enabling accurate prediction. This study concludes that ARIMA models offer a valuable tool for forecasting wheat production in India and China, provided that data quality, model specification, and external factors are appropriately considered. Accurate wheat production forecasts are crucial in providing food security and making informed agricultural and economic decisions in both countries. This analysis leads to the specific broader form of agricultural forecasting while emphasizes the potential for time series analysis to address agricultural challenges. For the most part, ARIMA (0,1,0) model appears to fit the data over India efficiently, and ARIMA (1,1,0) structure appears for delivering reasonable forecasts for the China wheat production time series. The forecasted values for the next 10 years are 2020 to 2029, along with 95% prediction intervals (Lo 95 and Hi 95).