A reasonable water supply is a prerequisite for potatoes to achieve high yield and high-quality tubers. To realize the rapid water diagnosis during the critical period of potato water demand, we used hyperspectral remote sensing and machine learning to study the real-time monitoring of plant water status during the potato tuber formation period to lay the foundation for efficient water management of potatoes in arid areas. This article aims to construct a quantitative estimation model for leaf water content during potato tuber formation with high monitoring accuracy and greater universality. The data of canopy hyperspectral reflectance and leaf water content were measured, and the characteristic spectral parameters that respond to the moisture content of potato leaf finally constructed the Partial least squares regression, Support vector machine, and BP neural network models of leaf water content based on the hyperspectral characteristic parameters. The results showed that: For the monitoring of potato leaf water content, screened the 13 sensitive bands such as 725, 856, 1 000 nm, etc; 11 characteristic spectral first-order derivatives such as 521, 555, 570 nm, etc.; and 7 characteristic spectral indices such as MSI, NDII, PSRI, etc. The three established models that are based on the above characteristic spectral parameters can accurately quantify the leaf water content of potatoes in the tuber formation stage, which means these combined spectral characteristic parameters have strong practicality; Moreover, the use of characteristic spectral parameters screened from full growth stage leaf moisture content and hyperspectral data had higher universality in quantitative monitoring of leaf water content in potato during the critical growth stages. The BP neural network model had the highest prediction accuracy in monitoring leaf moisture content during the tuber formation period. Therefore, this studys results can monitor potato leaves water content in real-time and accurately, which was of great value for evaluating the water status of potato plants and providing technical support for rapid water diagnosis and water-saving irrigation recommendations for potatoes.