Kerala's coffee is a major economic driver and lifeline for the state's farmers, making it one of the state's most important crops. In order to help with decision-making and resource management, producers and stakeholders in the coffee industry need precise and trustworthy prediction models due to the volatile nature of coffee production. The purpose of this research is to evaluate and compare different time series models for predicting coffee crop yields in Kerala. The purpose of this research is to evaluate the efficacy of advanced time series models like AutoRegressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and others by making use of historical data, climatic trends, and pertinent socioeconomic variables. Forecast accuracy, robustness, and relevance to real-world coffee farming scenarios are just a few of the factors that will be used in the comparison evaluation. The results of this study will shed light on the most effective time series modeling approaches for crop prediction in the coffee industry in Kerala, allowing farmers, stakeholders, and policymakers to improve coffee production methods and ensure the industry's long-term viability.