India, with a population of 1.40 billion, is the most populous country in the world, necessitating increased food production. For making decisions about issues relating to food security, reliable crop yield estimation is essential. Knowing the expected yield of one's standing crops is crucial to farmers and can be a complicated task in and of itself. Modern artificial intelligence algorithms have shown to be highly useful tools for accurately predicting agricultural production. The primary focus of this study is on predicting the yield of five important crops grown in the Nashik region. Crop yield is significantly influenced by climatic variables such as rainfall, minimum and maximum temperatures, relative humidity, and evaporation. In this regard, we used the Decision tree (M5tree) method to predict the yield of five important crops farmed in the Nashik region of Maharashtra state in India: rice, jowar, maize, groundnut, and sugarcane. With acceptable accuracy, the developed models have functioned well. The association between climatic variables and the agricultural production of the crops under study was disclosed by the decision trees, and the rule accuracy validated this relationship.