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The number of dairy cows in China has been steadily increasing over the years. Monitoring their physiological parameters and behavior is crucial to improve their production performance, economic benefits, and enhance the competitiveness of China’s dairy industry. However, traditional monitoring methods, such as using thermometers to monitor physiological parameters, can lead to stress reactions in cows. Additionally, behavioral monitoring relies mainly on manual observation, which is prone to errors due to differences in experience and low efficiency. Fortunately, with the advancement of artificial intelligence technology, equipment and technology for cow health monitoring are constantly evolving. The methods provided convenient and accurate means for cow health monitoring, overcoming the limitations of traditional methods. The advantages of the methods included being non-contact, stress-free, and highly efficient. The research progress in equipment and technology for monitoring the health of dairy cows both domestically and internationally were systematically analyzed and summarized. Specifically, it discussed the monitoring of physiological parameters such as body temperature, body size, weight, and respiratory frequency, as well as behavioral monitoring, including basic behaviors (such as standing, walking, lying down), rumination, and limping. In terms of physiological parameter detection, three methods for monitoring cow body temperature were compared. Implantation and contact methods had advantages such as high stability and low cost, while non-contact methods had a wide range of temperature measurement and can be used for non-invasive temperature measurement. The methods were analyzed based on two-dimensional and three-dimensional images, as well as the combination of two-dimensional and three-dimensional images, and concluded that the combination of two-dimensional and three-dimensional images can more accurately measure the size of cows. The relational model used in cow weight estimation was compared and analyzed, and the conclusion that multiple regression model can accurately estimate cow weight was drawn. The characteristics of sensors, cameras, infrared thermal imagers and other equipment used for monitoring cow respiration were analyzed, and their applicability was compared. In terms of cow behavior monitoring, the application principles, advantages and disadvantages of contact sensor monitoring and non-contact visual monitoring in cow behavior monitoring were summarized. Sensor monitoring had more advantages in monitoring accuracy, while visual monitoring had advantages such as high efficiency and non-invasive. In addition, typical health monitoring application systems in smart aquaculture both domestically and internationally were summarized and analyzed, and the characteristics and application scenarios of the application systems were introduced. Finally, in view of the problems faced by cow health monitoring methods, the need to further optimize data analysis and processing methods while improving equipment and technology was proposed, providing efficient tools that combine software and hardware for cow health monitoring, and providing sustainable development methods and reference ideas for cow health management and the breeding industry.