This review presents a comprehensive analysis of machine learning (ML) applications in agricultural production systems. The study categorizes ML applications into four main areas: crop management, livestock management, water management, and soil management. Each category includes several subcategories, such as yield prediction, disease detection, weed detection, crop quality, species recognition, animal welfare, livestock production, evapotranspiration estimation, and soil property prediction. The review analyzes 40 articles published between 2004 and the present, focusing on the use of ML in precision agriculture. In crop management, ML is used for yield prediction, disease detection, weed detection, crop quality assessment, and species recognition. For example, ML models have been applied to predict wheat yields, detect diseases in crops, and identify weeds using multispectral and hyperspectral imaging. In livestock management, ML is used for animal welfare monitoring and livestock production optimization. ML models help in predicting animal health, detecting behavioral changes, and improving production efficiency. In water management, ML is used for evapotranspiration estimation and irrigation system optimization. In soil management, ML is used for soil property prediction, including soil moisture, temperature, and organic carbon content. The review highlights the use of various ML models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning methods. These models are applied to sensor data, enabling real-time decision support for farmers. The study concludes that ML has significant potential to improve agricultural productivity and sustainability by enabling data-driven decision-making and optimizing resource use. However, the integration of ML into agricultural systems remains limited, and further research is needed to develop more efficient and scalable solutions.This review presents a comprehensive analysis of machine learning (ML) applications in agricultural production systems. The study categorizes ML applications into four main areas: crop management, livestock management, water management, and soil management. Each category includes several subcategories, such as yield prediction, disease detection, weed detection, crop quality, species recognition, animal welfare, livestock production, evapotranspiration estimation, and soil property prediction. The review analyzes 40 articles published between 2004 and the present, focusing on the use of ML in precision agriculture. In crop management, ML is used for yield prediction, disease detection, weed detection, crop quality assessment, and species recognition. For example, ML models have been applied to predict wheat yields, detect diseases in crops, and identify weeds using multispectral and hyperspectral imaging. In livestock management, ML is used for animal welfare monitoring and livestock production optimization. ML models help in predicting animal health, detecting behavioral changes, and improving production efficiency. In water management, ML is used for evapotranspiration estimation and irrigation system optimization. In soil management, ML is used for soil property prediction, including soil moisture, temperature, and organic carbon content. The review highlights the use of various ML models, including artificial neural networks (ANNs), support vector machines (SVMs), and ensemble learning methods. These models are applied to sensor data, enabling real-time decision support for farmers. The study concludes that ML has significant potential to improve agricultural productivity and sustainability by enabling data-driven decision-making and optimizing resource use. However, the integration of ML into agricultural systems remains limited, and further research is needed to develop more efficient and scalable solutions.