<p>This study investigates the properties of SnO₂ nanomaterials as CO₂ adsorbents. Although CO₂ capture technologies have significantly advanced, challenges such as high costs and limited scalability persist. In recent years, nanomaterials have emerged as promising candidates for CO₂ adsorption due to their high adsorption capacity, lower cost, and wide availability. However, future research should focus on developing low-cost and efficient nanomaterials to enable large-scale industrial CO₂ capture. This study examined the CO₂ adsorption capacity using SnO₂ nanoadsorbents by employing response surface methodology (RSM) and artificial neural networks (ANNs), specifically radial basis function (RBF) and multilayer perceptron (MLP) networks, for process modeling and optimization. Through the analysis of experimental data, temperature, pressure, and adsorption time were identified as crucial influencing factors. The R² value of 0.9933 for RSM indicated a great match, whereas the R² value of one for ANNs indicated superior predictive accuracy. With a minimum mean squared error (MSE) of 0.00012 for the dataset, the MLP was trained using a three-layer activation function. With 288 neurons and a spread of 2, the RBF network achieved an R² value of 0.9985 and a minimal MSE of 0.00075. The smooth MLP plots effectively captured complex discontinuities, showcasing the superior predictive abilities of ANNs for optimizing the CO₂ adsorption process using SnO₂ nanoadsorbents, while the RSM surfaces exhibited rigid, polynomial-based patterns.</p>