This study explores the application of artificial neural networks (ANNs) to predict the concentration of total reducible sugars (TRS) in hydrolysates derived from pretreated mixed agro-waste, with implications for environmental engineering, biotechnology, and sustainable biorefinery processes. Experimental data were generated using bench-scale hydrolysis experiments conducted at the Bioresource Engineering Research Group la-boratory and the Centre for Proteomic and Genomic Research (Cape Town, South Af-rica). Nepenthes mirabilis (N. mirabilis) pitcher fluids, sampled from Pan’s Carnivores Plant Nursery (Cape Town, South Africa), served as enzymatic agents for agro-waste pretreatment. The ANN analysis was conducted using MATLAB’s Neural Network Toolbox, employing a feed-forward topology with a 1-5-2-2 network structure. Input parameters included particle size and enzyme fraction, while outputs corresponded to TRS concentrations at 24 and 72 hours. Validation identified three epochs as optimal for model training, albeit with a mean squared error of 0.54. Experimental runs coded 2, 3, and 4 demonstrated minimal prediction errors (<5%), though runs 3 and 4 exhibited high phenolic concentrations, which are undesired in TRS hydrolysates destined for fer-mentation. In contrast, Run 12 (>106 µm particle size/>3 kDa enzyme fraction) showed promising predictability (R² = 0.93) with low phenolic content, highlighting its suitabil-ity for future biorefinery applications. Statistical validation of the ANN predictions against experimental TRS data confirmed the model’s robustness. This research pro-vides a foundation for optimizing agro-waste pretreatment processes using N. mirabilis pitcher fluids and advances studies through ANN-based modeling. The dataset offers a platform for researchers to explore alternative enzymatic agents or integrate advanced optimization software for scalable bioresource valorization.