India is an agricultural country, much of the economy is dependent on productivity growth. Agriculture is heavily dependent on rainwater and depends on various soil conditions, namely nitrogen, phosphorus, potassium, and climates such as temperatures and rainfall. The growth of agricultural technology will increase crop production. Machine learning is a promising area for research to anticipate yield based on data patterns. The proposed learning algorithms apply to the machine learning algorithms: Random Forest, Logistic Regression, Decision Tree, and Support Vector Machine. Predictions of plants that are most relevant to the current environment are being made. This work gives producers a strong prediction of planting what types of crops in their area on the farm according to the above-mentioned parameters to grow a smart agricultural product. four different algorithms are applied in this project system. With the help of the ROC-AUC-SCORE, the accuracy of all the models is compared and other factors like precision, recall, F1 score, and support are also compared. And from all these results we can know which model is perfect and from that, we can know which crop is suitable for the given soil and climatic condition.