Peanut oil is susceptible to aflatoxin B1 (AFB1) contamination during the production process. In response to the problems of tedious operation and poor timeliness of the traditional detection method for AFB1, fluorescence spectroscopy was utilized for the rapid determination of AFB1 in peanut oil. Firstly, the optimal excitation wavelength was determined by three-dimensional fluorescence spectroscopy. K-means and self organizing map (SOM) clustering algorithm were used to qualitatively identify the AFB1 content in peanut oil with an accuracy of over 95%. Nextly, two preprocessing algorithms and two dimensionality reduction algorithms were used. Competitive adaptive reweighted sampling (CARS) was selected as the best wavelength selection method. The echo state network (ESN) was then used for quantitative modeling of AFB1. Compared with other models, the results showed that the CARS-ESN model obtained the best prediction of AFB1 content. Finally, the sparrow search algorithm (SSA) was used to find the optimal ESN parameters. The final test set coefficient of determination reached 0.984, with root mean square error of 2.13μg/kg, demonstrating the feasibility of fluorescence spectroscopy technique combined with ESN to predict the AFB1 content in peanut oil. The research result can provide a theoretical basis for the development of an online system for the detection of fungal toxin content in edible oils.