With the rapid development of large-scale beef breeding in our country, modern smart beef breeding technology, including the Internet of Things, big data and artificial intelligence, has been continuously improved. The identification of individual beef cattle and real-time monitoring of production performance, such as body weight, body size, and feed intake, are crucial for improving feeding management, reducing personnel workload, and accelerating the breeding process of beef cattle. Individual identification is the foundation for monitoring individual production performance. Current methods primarily rely on RFID identification technology and image-based deep learning individual identification technology. While RFID offers high accuracy, it faces challenges such as high cost, short identification distance, and significant workload for tagging. Image-based deep learning identification technology identifies individuals by analyzing unique biometric features like body surface patterns, ear tag text, nose prints, iris, retina, facial features, and side profiles. However, its effectiveness can be affected by lighting conditions and individual differences. In the future, it is necessary to develop precise, rapid and dynamic recognition machine vision recognition technology for beef cattle that can adapt to different environmental conditions. Images captured by 2D and 3D cameras can be used for estimation of body size and weight after key feature extraction and analysis. 2D cameras have the advantages of simple equipment acquisition and low cost. However, its reliance on reference objects of known dimensions during the measurement process, as well as the measurement limitations of curved surface characteristic body size indicators such as chest circumference and abdominal circumference, directly affect the accuracy of related body size measurement and body weight estimation. In contrast, 3D cameras can obtain the external structure of beef cattle and the distance information between them and the equipment in a comprehensive and three-dimensional manner, thus providing the possibility for precise measurement of multi-dimensional body weight indicators. Automated monitoring of beef cattle feed intake is vital for assessing feed efficiency. Automatic weighing feed troughs accurately measure intake by calculating the weight difference before and after feeding using pressure sensors. However, the challenges such as high installation costs and operational inconvenience have largely confined their application scope. Feed intake can also be effectively estimated through depth image changes before and after feeding or by recording feeding behavior by using relevant sensors. Nevertheless, in practical applications, the complexity of feed composition can affect monitoring accuracy. The technology for determining the production performance of beef cattle based on machine vision has made remarkable progress. However, it still faces many challenges, such as large amounts of data processing, environmental interference affecting the accuracy of results, and insufficient data development and utilization. In the future, strategies such as edge computing technology and optimizing phased detection can be adopted to reduce the computing pressure of device data and improve the agility of system response. Exploring 3D reconstruction technology based on single-view depth cameras could improve the feasibility of applying body dimension and weight monitoring in practical production settings. Efforts should be dedicated to developing universal prediction models applicable to different breeds and various growth stages to enhance the versatility and practicality of the technology. Strengthening multimodal data fusion will improve the comprehensive application of beef cattle production performance monitoring data. Intelligent monitoring technology is the key to the modernization of beef cattle breeding. Through technological innovation and integration, it is expected to achieve low-cost, high-precision and widely applicable intelligent monitoring technology for the production performance of beef cattle, promoting the intelligent upgrade of the beef cattle industry, improving production efficiency and economic benefits, and meeting market demands. This review summarized intelligent monitoring technologies for beef cattle identification, as well as for estimating body weight, body size, and feed intake. It also discussed the challenges and future development trends of intelligent monitoring technology for beef cattle production performance in China, aiming to provide references for the research and application of related intelligent monitoring technologies.