The prediction of yield and crop recommendation in the field of agriculture depends on the agro climatic conditions, which determines the success ratio of the crops. The minor changes in climatic condition will also affect the crop growth, production and yield. Analyzing the different climatic patterns over the years and predicting the future agro climatic condition is still a difficult task. Many machine learning prediction of climatic conditions are used over the decades, here to get better optimal results the concept of time series forecasting is taken for validating and predicting the data. the paper is dealt with the different attributes of agro climatic conditions like vapor pressure, precipitation, maximum temperature, minimum temperature average temperature are analyzed by different time series models and its accuracy are compared to find the optimal model for forecasting. © 2020 Alpha Publishers.