Strip mining is considered one of the most used techniques for the extraction of stratified mineral deposits close to the surface. Despite the fact that this method is considered efficient, it involves a step-by-step removal of overburden, which disturbs large areas of land in the extraction zone. Over time, there has been a growing concern about the environmental footprint of strip mining, particularly when operations involve thick layers of overburden that are difficult to remove and manage. The assessment of these impacts with precision is still a challenge, since no standard or widely recognized method can fully reflect the variability and the complex conditions found in real mining sites. In this work, the use of artificial intelligence techniques, specifically artificial neural networks (ANNs), is explored as a potential approach for the prediction of the environmental effects of strip mining under thick overburden conditions. The conception of this framework integrates essential operational variables with local geological characteristics in order to develop an ANN model containing a single hidden layer, which is designed to identify non-linear relationships among the considered factors. Even though empirical data has not been integrated at this stage, the model is designed to illustrate how operational parameters can be linked with anticipated environmental results. The principal goal of this study is to investigate how possible it can be to utilize AI-based models to predict the environmental impacts generated by the mining operations under different geological and technical conditions.