The understanding of soil erodibility is essential for mitigating soil degradation and sustainable land management strategies. While various studies have examined the behavior of soil blended with waste materials, such as fly ash, limited work has given emphasis to the soil erodibility prediction using Artificial Neural Network (ANN). This study investigates the efficient application of fly ash stabilized red soils and its potential to reduce soil erodibility. The Red Soil (RS) samples and Red Soil Fly Ash (RS-FA) mixes at suitable proportions are characterized, and their properties, such as Unconfined Compressive Strength (UCS) and erodibility indices, are analyzed. Results reflect that the incorporation of 15–20% RS-FA mixes enhances the Unconfined Compressive Strength (UCS). Further, the ANN models are developed by varying the training algorithms to predict soil erodibility on laboratory-tested soil properties. The input layer is comprised of moisture content, plastic limit, and unconfined compressive strength, and the outer layer shows the soil erodibility indices. Among the various ANN models with varying internal configurations, the resilient backpropagation training algorithm yielded the most accurate prediction. The statistical parameter, i.e., mean square error of resilient backpropagation, shows a strong predictive ANN model (RP10TAN) with the value of 0.99751 (red soil +0% fly ash), 0.99974 (red soil +15% fly ash), and 0.9999 (red soil +25% fly ash). Also, this demonstrates the important role of training algorithm selection in the prediction of soil erodibility. The study’s findings support the integration of ANN-based models for the nonlinear behavior of soil parameters and promote the use of industrial by-products (fly ash) as a sustainable stabilization agent.