The purpose of this research was to evaluate and contrast time series models for predicting millet yield in the context of agricultural output in Tamil Nadu, India. The study's overarching goal is to shed light on the prediction capacities of ARIMA (AutoRegressive Integrated Moving Average) and SARIMA (Seasonal AutoRegressive Integrated Moving Average) models as they pertain to millet farming by examining their respective applications. The purpose of this research is to determine the efficacy of the ARIMA and SARIMA models in capturing the nuances of millet yield fluctuations by using historical data covering various influential factors like climatic variations, soil conditions, and agricultural practices. To further develop prediction approaches for sustainable agricultural planning and decision-making, this comparison sheds light on seasonal trends, trend changes, and other dynamic components driving millet output. In order to maximize millet output and guarantee food security in the Tamil Nadu area, this study is crucial in promoting the implementation of data-driven solutions.