aspg-3-2851-1791417578 20261007235938 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 16 2 Bryan Barragán-Pazmiño Technical University of Cotopaxi.. Cotopaxi, Ecuador Angel Ordóñez Echeverría Higher Polytechnic School of Chimborazo, Research and Development Group for Environment and Climate Change (GIDAC), Riobamba, Ecuador Magdy Echeverría Guadalupe Higher Polytechnic School of Chimborazo, Research and Development Group for Environment and Climate Change (GIDAC), Riobamba, Ecuador Theofilos Toulkeridis University of the Armed Forces ESPE, Sangolquí, Ecuador Machine learning approaches are utilized to identify patterns in behavior and generate predictions across various applications. The objective of this work is to create a highly efficient model for accurately measuring and analyzing the levels of soil organic carbon (SOC) in the Chambo river sub-basin, which is situated in the province of Chimborazo. The model evaluation entails the application of diverse machine learning algorithms and approaches to determine the most efficient regression model. Regression models are improved using techniques such as Artificial Neural Networks, Support Vector Machines, and Decision Trees. The Resilient Backpropagation method yields the most precise model, as it accounts for a greater proportion of the variability in SOC content for the test data. This aligns with the findings from the training data, demonstrating a relatively low mean absolute error and a processing time that is approximately 400 times faster than that of the Multilayer Perceptron algorithm. The evaluation of estimating models is an objective procedure that considers not only the findings and precise metrics derived from the model's design, but also other relevant elements. The effectiveness of the Random Forest approach, specifically the quantile regression forests technique, has been established for estimating SOC contents in the Chambo river sub-basin data. 2024 22 31 2851 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.160202 https://www.americaspg.com/journal/3/article/2851 https://www.americaspg.com/storage/01736276412.docx