This study examines the use of time series analysis for predicting maize production in India. The objective is to analyze the relationship between maize productions, domestic consumption, exports, and to forecast maize production using various time series models. The study employs cointegration techniques such as Johansen's test, Engle-Granger test, and Granger causality test to determine the long-term relationship between the variables. The findings exhibit that there is a bidirectional causal relationship between domestic consumption and export series and between domestic consumption and production series and that all three variables are co-integrated. To forecast maize production, the study employs both multivariate and univariate time series models. The multivariate models used are vector auto regressive and vector error correction models, while the univariate models used are ARIMA (auto regressive integrated moving averages), Holts exponential smoothing, NNAR (neural network auto regression), K-nearest neighbors (KNN), and LSTM (long short-term memory). The best forecast model is selected on the basis of a comparison of three evaluation metrics: mean absolute square error, mean absolute percentage error, and root mean square log error.