Using the above system, farmers can be guided in the choice of crops to be grown by predicting the suitable ones for a given region by incorporating supervised ML algorithms. The seed data of the crops are collected here, with the appropriate parameters like temperature, humidity, soil Ph, rainfall, nitrogen, potassium and phosphorus content which supports its maximal growth. Based on the obtained data the crop prediction is carried on. This is provided along with the performance metrics of the system. Performance of Naïve bayes Classifier, AdaBoost Classifier, Decision Tree Classification, Voting Classifier algorithms are compared and based on the accuracy, precision, recall, F1 score and confusion matrix, decision tree classifier algorithm is chosen as the best algorithm for crop prediction and the model is hence deployed with it.