Polyethylene glycol (PEG) is one of the most commonly used seed initiators, in order to regulate the seed water uptake during seed priming. This study aims to investigate the effect of seed water uptake on the priming behavior under different PEG concentrations and priming times using low-field nuclear magnetic resonance (LF-NMR) technology. The aqueous phase state of maize seeds was dynamically monitored to be initiated by the different PEG concentrations. A prediction model of maize seed germination was constructed to clarify the relationship between seed water uptake and priming effect using machine learning. LF-NMR data was collected from the maize seeds at each node of priming time. A systematic analysis was carried out to determine the effects of PEG priming on seed germination and seedling growth. The optimal conditions of priming treatment were determined to evaluate the physical and chemical indicators, such as the vigor index of maize seeds. The pattern of water uptake was obtained in the maize seeds during priming. A regression model was also constructed to predict the priming effect of maize seeds. The main findings were summarized as follows: Firstly, there were some effects of PEG priming on the germination and seedling growth of maize seeds. Two-way ANOVA analysis showed that there were highly significant effects of PEG priming time and priming concentration on the germination indexes of maize seeds. Among them, the effect of priming time on the germination indexes of maize was much larger than that of priming concentration. With the increase of priming time (0-48 h), the viability of maize seeds showed a tendency to increase and then decrease, where the most suitable priming time was 16 h. Secondly, the water uptake of maize seed was elucidated during priming. The internal water of maize seeds was divided into two kinds of water components: bound water (0.1 ms < T₂₁ < 20 ms) and free water (20 ms < T₂₂ < 400 ms), according to the length of LF-NMR T₂ relaxation time. With the increase of priming time (0-48 h), the content of bound water increased and then gradually stabilized, while the content of free water continued to rise. The total water content increased and then gradually slowed down. The content of bound water had just entered the stagnation stage, while the increasing trend of A₂₁ signal amplitude decreased basically without increase, indicating an appropriate indicator to judge the priming time of maize seeds. Finally, the medium Gaussian support vector machine (SVM) model and exponential gaussian process regression (GPR) model were constructed to assess the priming effect of maize seeds, respectively, according to the data before and after the screening of LF-NMR feature parameters. Among them, the exponential GPR model was achieved in the validation set R² of 0.920 for maize seed germination after feature screening, which was better than 0.900 before that. The water absorption kinetics of the seed was elucidated to verify the priming efficacy. The experiment proved that the moisture variations during seed priming detected by LF-NMR can be expected for the rapid screening of the optimal priming conditions. The finding can also provide a new idea for the parameters setting and rapid evaluation during corn seed priming.