Machine learning techniques significantly affect the economy via intelligent data utilization through data transformation and decision-making. Considering the worldwide food supply crisis, agriculture has become one of the most critical factors. Therefore, using modern technologies in artificial intelligence and machine learning in this field is a must. This work suggests an agricultural recommender system that uses rainfall data for an efficient crop recommender system. There are countless potential advantages to combining machine learning techniques in modern agriculture. The main focus of these algorithms is to minimize waste and assist in maximizing crop production via rational decisions concerning crop harvesting, planting, and irrigation. This paper conducts a comprehensive comparative analysis of three hyperparameter tuning methods. These techniques are used to enhance the accuracy of 8 classification methods, which are the AdaBoost classifier, decision tree (DT), Gaussian NB, MLP classifier, random forest (RF), SGD, support vector machine (SVM), and XGB. The suggested machine learning models are used to construct a recommender system capable of analyzing the rainfall data to predict suitable crops that match the rainfall quantities. Performing appropriate hyperparameter tuning for a machine learning classifier can lead to a substantial increase in accuracy. The performance of the classifiers is investigated via the utilization of the input rainfall dataset after the hyperparameter tuning process. A detailed analysis of the advantages and disadvantages of each single tuning method is described. The results exhibited that the random forest model gave the best accuracy. It showed the utmost precision, attaining a score of 0.999158, obtained through hyperparameter tuning using Bayesian optimization.