<p>Ozone, a generally recognized as safe (GRAS) disinfecting agent, can be used to inactivate significant spoilage-causing microbes while storing agricultural produce. However, ozone degradation kinetics differ considerably in the presence of biological commodities due to the present reactive active sites on them. Therefore, the study of dissociation in the presence of agricultural commodities helps optimize the ozone application’s design parameters in storage structures for horticultural crops like onions. In the present study, a recurrent neural network (RNN) model was developed to predict the residual ozone concentration. The RNN trained using the Levenberg–Marquardt backpropagation algorithm demonstrated strong performance with both the ‘tansig’ and ‘logsig’ transfer functions. The network achieved high accuracy, as indicated by a correlation coefficient (<i>R</i>-value) of 0.997 to 0.999, reflecting an excellent fit between the predicted and actual values. The root-mean-square error (RMSE) was also recorded between 0.100 and 0.418 × 10⁻¹, emphasizing the model’s precision in minimizing prediction errors and accurately capturing the underlying data patterns. The sensitivity, specificity, and mean AUROC curves were obtained to be 92.5%, 67.1%, and 0.782, respectively. The AUROC value above 0.5 reassures the successful learning of features by the model, making it a reliable predictor of residual ozone concentration.</p>