The paper provides a comprehensive review of the application of machine learning (ML) in agricultural production systems, categorized into four main areas: crop management, livestock management, water management, and soil management. The authors analyze 40 articles published in various journals, focusing on ML models and their implementation in these agricultural domains. Key ML models discussed include regression, clustering, Bayesian models, instance-based models, decision trees, artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning. The review highlights the benefits of ML in improving precision agriculture, such as yield prediction, disease detection, weed management, and soil condition estimation. The most frequently used ML models are ANNs and SVMs, with ANNs being particularly popular in crop management and SVMs in livestock management. The paper concludes by discussing the future potential of integrated ML tools in agriculture, emphasizing the need for better integration with decision-making processes to enhance production levels and product quality.The paper provides a comprehensive review of the application of machine learning (ML) in agricultural production systems, categorized into four main areas: crop management, livestock management, water management, and soil management. The authors analyze 40 articles published in various journals, focusing on ML models and their implementation in these agricultural domains. Key ML models discussed include regression, clustering, Bayesian models, instance-based models, decision trees, artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning. The review highlights the benefits of ML in improving precision agriculture, such as yield prediction, disease detection, weed management, and soil condition estimation. The most frequently used ML models are ANNs and SVMs, with ANNs being particularly popular in crop management and SVMs in livestock management. The paper concludes by discussing the future potential of integrated ML tools in agriculture, emphasizing the need for better integration with decision-making processes to enhance production levels and product quality.