Crop diseases continue to be a serious danger to the world's food supply and the viability of agriculture. Modern agriculture presents several obstacles that traditional disease detection and control techniques often are unable to handle. Machine learning (ML) has recently become a potent tool for revolutionizing the way we identify, control, and lessen the effects of crop diseases. Based on academic articles and field data, this study offers a thorough analysis of the status of machine learning applications in agricultural disease identification and management. The relevance of crop disease management in terms of food security, economic effect, environmental sustainability, and human health is highlighted in the opening paragraphs of the study. In order to satisfy the needs of a rising global population, it emphasizes the urgent need for effective and long-lasting illness management techniques. The article then explores the numerous ways that agricultural disease detection uses machine learning. Utilizing deep learning algorithms and image-based detection, automatic analysis of cropped photographs can now accurately identify illness signs. Early illness diagnosis based on variables like temperature, humidity, and spectral signatures has benefited greatly from sensor-based detection, which uses data from environmental sensors, drones, and satellites. The treatment of agricultural diseases using machine learning is also covered in the article. Farmers may use real-time advice from decision support systems that use machine learning algorithms to optimize disease control plans and resource allocation. By customizing resource application based on sensor and remote sensing data, precision agriculture, powered by ML, improves resource efficiency and agricultural yields. Additionally, risk assessment and illness predictions have benefited from machine learning. In order to forecast disease outbreaks and evaluate risk, machine learning (ML) algorithms examine historical disease data, weather patterns, and environmental factors. These analyses provide farmers timely warnings and suggestions. Robotics and agricultural automation combined with machine learning have enabled autonomous illness diagnosis and treatment, cutting labor costs and increasing productivity. ML algorithms are increasingly being used in robots and drones that can patrol fields and detect illness indicators. In the paper's conclusion, the difficulties in applying machine learning techniques to agriculture are acknowledged. These difficulties include issues with data quantity and quality, model generalization, interpretability, infrastructure, and economic concerns. It highlights how crucial it is to overcome these difficulties by creating standardized datasets, better interpretability, and user-friendly interfaces for farmers. The use of machine learning tools in agriculture is anticipated to increase as research in the area develops, creating more robust and effective agricultural systems. The study focuses on the potential of machine learning to support sustainable agricultural practices, improve food security, lessen economic losses, and empower farmers. In pursuit of a more sustainable and secure food future, it highlights the revolutionary influence of machine learning on crop disease diagnosis and control.