The forecasting of yields is a crucial topic in agriculture. The farmer is curious to know how much production he might anticipate. In the past, farmer experience with a certain field and crop was taken into account when predicting production. The yield forecast is an important problem that needs to be solved based on the facts at hand. In agriculture, a variety of data mining techniques are used and evaluated to project crop productivity for the following year. The study presents a method for estimating agricultural productivity based on historical information. Applying association rule mining to agricultural data allows for this. The goal of the study is to develop a prediction model that can be applied to forecast future agricultural yield. This study provides a concise evaluation of crop yield forecast using data mining technique based on association rules for the selected location, the district of Tamil Nadu in India. The experimental results show that the proposed approach anticipates agricultural yield production accurately. The goal of this study is to immediately advise farmers on how climatic circumstances can vary so they can take the necessary steps to maximize agricultural yield production. The study uses a classifier ensemble approach to estimate crop productivity based on climatic variability, with an emphasis on the coastal region of India.