Industrial processes benefit the polysaccharides constituting the lignocellulosic biomass. However to manipulate them it is necessary that the biomass is submitted to the pre-treatment process. This is one of the most expensive and relevant steps for the arrangement and application of lignocellulosic fractions. The present study consists of a detailed investigation of the pretreatment process of lignocellulosic biomass with H2O2, applying intelligent technologies that enabled the optimization of this process. Artificial intelligence tools prove to be advantageous in solving the bottlenecks associated with technological advances. They enable the mathematical modeling of a process with maximum efficiency, optimizing its productivity, transforming experimental data into useful information and demonstrating the infinite possibilities of the relationships of the variables involved. The independent variables studied were the temperature (25-45 Celsius degrees) and the concentration of hydrogen peroxide (1.5 - 7.5 percent m / v). Qualitative analytical techniques (Raman and FTIR) and quantitative (Klason method) were applied to produce a database for the extraction of lignin with H2O2, which was used in the development of neural models applying Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). And polynomial models, which had their parameters estimated by Genetic Algorithms (GA). The models developed were able to predict: the Extracted Lignin Content (percent) by Raman Spectroscopy, the Oxidized Lignin Content (percent) by FTIR, the Residual Lignin Content (percent) by the Klason Method, and lastly, two models for the comparison of the qualitative analytical response with the quantitative analytical response. The polynomial models, which had their parameters estimated by GA, were statistically evaluated using ANOVA and correlation coefficient (R2) evaluated the polynomial models developed by GA statistically. And the neural models developed were evaluated by the coefficient of correlation (R2), number of parameters and error indexes (SSE, MSE and RMSE). For each proposed polynomial and neural model, when coherent, response surfaces and contour curves were plotted allowing the identification of the most suitable operational region for the pretreatment with H2O2. Among the proposed intelligent strategies, the models developed with ANN proved to be more efficient for the predictions related to lignin extraction.