In recent years, magnetic nanoparticles (MNPs) have been widely used as a new material in biomedicine and other fields due to their broad versatility, and the quantitative detection method of MNPs is significantly important due to its advantages in immunoassay and single-molecule detection. In this study, a method and device for detecting the number of MNPs based on weak magnetic signal were proposed and machine learning methods were applied to the design of MNPs number detection method and optimization of detection device. Genetic Algorithm was used to optimize the MNPs detection platform and Simulated Annealing Neural Network was used to explore the relationship between different positions of magnetic signals and the number of MNPs so as to obtain the optimal measurement position of MNPs. Finally, Radial Basis Function Neural Network, Simulated Annealing Neural Network, and partial least squares multivariate regression analysis were used to establish the MNPs number detection model, respectively. Experimental results show that Simulated Annealing Neural Network model is the best among the three models with detection accuracy of 98.22%, mean absolute error of 0.8545, and root mean square error of 1.5134. The results also indicate that the method and device for detecting the number of MNPs provide a basis for further research on MNPs for the capture and content analysis of specific analyte and to obtain other related information, which has significant potential in various applications.