The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in cocoa-producing regions such as Santander and Norte de Santander, Colombia. These by-products, constituting approximately 70% of the fruit’s total weight, are currently underutilized, generating pollution and wasting resources with high valorization potential. This article proposes the design and rigorous experimental validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), both exhibiting identical coefficients of variation (6.25%). Furthermore, the integration of an optimized extraction sequence combined with neural networks allows for maximizing by-product yields while reducing final residue generation by 40%. This tool not only contributes to the circular economy and alignment with the Sustainable Development Goals (SDGs 9 and 12) but also offers a tangible pathway to improve the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.

The inefficient management of agro-industrial residues, particularly cocoa pod husk and mucilage, represents a critical environmental and economic challenge in Santander and Norte de Santander, Colombia, where over 55,000 tons of biomass waste are generated annually from a regional production exceeding 23,500 tons of cocoa beans. Furthermore, these residues, which constitute approximately 70% to 75% of the fruit’s total weight, are currently underutilized, generating severe localized pollution through uncontrolled decomposition and causing substantial economic losses by wasting compounds with high valorization potential. This article proposes the design and systematic experimental calibration and internal cross-validation of an empirical model based on artificial intelligence capable of predicting quantities of valuable compounds, including bioethanol, essential oils, paraffins, antioxidants, and pectins, obtained from cocoa residues. The model integrates critical variables such as cocoa variety, extraction methods, and process conditions, incorporating advanced machine learning techniques trained on a 100% empirical database of eighty-four (84) laboratory trials, combined with a post-inference sensitivity analysis via the Monte Carlo method with 10,000 simulations. Preliminary results demonstrate significant varietal differences; for instance, the CCN-51 variety achieves a mean bioethanol yield of 79.30 ± 4.96 mL/kg with a 95% confidence interval of (69.44–88.93) mL/kg, while the Criollo variety reaches 43.55 ± 2.72 mL/kg (38.14–48.84 mL/kg), exhibiting highly synchronized coefficients of variation of 6.255% and 6.246%, respectively. Furthermore, the integration of a cascading extraction sequence combined with neural networks may potentially contribute to maximizing by-product yields, with theoretical mass balance estimations suggesting a possible reduction of up to 40% in final residue generation compared to conventional isolated extraction pathways, as detailed in the proposed framework. This tool could potentially support the circular economy and alignment with Sustainable Development Goals 7, 9, and 12, while offering a possible pathway to contribute to the competitiveness of the Colombian cocoa industry through data-driven decision-making and sustainable technology adoption.