Purpose <p>This study investigates the drying kinetics of wild bitter gourd using a hot air oven at three different temperatures. Moreover, mathematical and ANN modeling were performed to demonstrate the contrast analysis and applicability of these models in predicting ongoing drying behavior parameters.</p> Method <p>The effects of drying temperature on various ongoing drying parameters were investigated and fitted with classical mathematical models, and ANN modeling was utilized to predict these parameters. A multilayer feed-forward ANN has been employed with transfer functions, namely “tansig” and “purelin,” and the training algorithm “trainlm” for single- and multi-variable predictions, i.e., moisture ratio, drying time, shrinkage ratio, surface, and center temperature.</p> Results <p>It indicates that drying at higher temperatures led to faster moisture reduction and shorter drying time, with the final moisture content ranging from 4 to 6%. The shrinkage ratio increased with temperature, reaching a maximum of 85%. Effective moisture diffusivity ranged from 7.51 × 10⁻¹⁰ to 1.50 × 10⁻⁹ m²/s, while the activation energy was estimated to be 33.85&#xa0;kJ/mol. In the classical model, the Wang and Singh model showed higher fitting accuracy, i.e., ≥ 0.9990. ANN modeling results indicate strong ability to predict ongoing drying parameters with a strong overall correlation coefficient ranging from 0.8423 to 0.9995 for single-variable prediction and from 0.8127 to 0.9997 for multi-variable prediction.</p> Conclusion <p>It is concluded that the ANN model has better prediction ability in the case of multi-variable prediction scenarios. Moreover, findings demonstrate the potential of ANN modeling for predicting the non-linear drying characteristics, offering insights into its effective utilization and long-term storage.</p>