Abstract : This study aims to assess the reliability of crop production under diverse environmental conditions and utilizes regression modeling for Foxtail millets growth prediction, a focal point of this investigation. Machine learning is an emerging field in agricultural research, particularly in the analysis and forecasting of Foxtail millets growth yields. The process of crop production is impacted by a multitude of factors such as the number of days to flowering, maturity period, plant height, and fodder yield, among others. In this research, machine learning techniques, particularly linear regression, have been employed to forecast Foxtail millets yield. Linear regression was chosen due to its effectiveness as a predictive model, demonstrating a notably higher accuracy for this dataset in comparison to alternative models. Complex datasets that pose challenges for conventional analysis methods can be effectively decoded using machine learning strategies, uncovering valuable underlying patterns automatically. This enables informed decision-making processes by revealing unseen knowledge and patterns related to various agricultural challenges. Furthermore, machine learning facilitates the prediction of future events. During the growing season, farmers are keen on estimating their expected yield. With the continuous increase in agricultural data volume globally, this paper focuses on predicting crop yields using collected agricultural datasets. The research employs a regression analysis model to evaluate the accuracy and efficacy of predicting Foxtail millets crop yields in India. Linear regression is utilized to establish correlations between mean, variance and Foxtail millets yield. Assessing the potential millet production rate is crucial for farmers to benefit from predictive outcomes and mitigate financial losses. The research findings highlight the accuracy of Foxtail millets yield predictions using the regression model.