Maize production and consumption in Bangladesh are increasing as an alternative to wheat for sustainable food security and economic growth. An authentic crop production forecasting method is crucial for securing food security through proper agricultural policy-making, especially in developing nations like Bangladesh, where most people depend on agricultural farming. The present study predicted the time-series analysis of maize cultivation area, yield, and production employing the Autoregressive Integrated Moving Average (ARIMA) model. Stationarity assessments were done utilizing the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) and Augmented Dickey–Fuller (ADF) tests at a 5% significance level using yearly data from 2009 to 2024 collected from Index Mundi. The fit model was chosen based on the lowest value of Bayesian Information Criterion (BIC), the Corrected Akaike Information Criterion (AICc), and the Akaike Information Criterion (AIC). Ljung–Box and Jarque–Bera tests were utilized to detect autocorrelation. The metrics MAE, RMSE, MASE, and MAPE were used to verify values. After reviewing all criteria, ARIMA (2,1,0) was identified for the production area, ARIMA (0,1,0) for yield, and ARIMA (1,1,1) for production. The forecast specified a steady increase in the production area, with a compound annual growth rate (CAGR) of 8.1%. Yield is anticipated to remain stable at 9.0 tons per hectare, reflecting the ongoing utilization of high-yielding hybrids and advanced agronomic techniques. It is forecasted that by 2029, maize production will reach around 9.04 million metric tons, with a compound annual growth rate (CAGR) of 10.0%. The results underscore the importance of the increasing maize production trend in Bangladesh's food and feed sector, particularly as a vital resource for the dairy, fishery, and poultry sectors. Furthermore, they demonstrate the reliability and predictability of ARIMA models, thereby assessing their relevance in agricultural planning and judicious decision-making in the face of market and climatic uncertainty.