This study employed artificial olfaction–vision equipment combined with multidata fusion and deep learning technologies to accurately predict the freshness of chilled beef, providing a theoretical basis and methodological support for the rapid detection of meat product freshness. In this study, a self-developed electronic nose and machine vision system was used to collect odor and image information from beef samples. The semi-micro Kjeldahl method and an acid–base meter were used to determine the total volatile basic nitrogen content and pH of the samples, which served as the basis for evaluating the freshness of the beef samples. Firstly, kernel principal component analysis (KPCA) was used to reduce the dimensionality of the olfactory–visual feature information of beef. Subsequently, prediction models for the electronic nose, machine vision, and data fusion were established using the backpropagation neural network (BPNN), support vector machine (SVM), convolutional neural network (CNN), and slime mold algorithm–convolutional neural network–bidirectional gated recurrent unit with attention (SMA–CNN–BiGRU-Attention) methods, and the model results were compared. The results indicated that the prediction performance of the data fusion model was significantly better than that of a single electronic nose or machine vision model. Among them, the SMA–CNN–BiGRU-Attention data-fusion model exhibited optimal stability, 100% accuracy in classifying the three freshness levels of beef, and 96.4% accuracy in classifying beef stored under chilled conditions for 12 days. This study demonstrates the feasibility of artificial olfaction–vision technology for detecting the freshness of meat products. The SMA–CNN–BiGRU-Attention model, based on data fusion, achieves high accuracy, offering a potential solution for detecting beef freshness.