Soil degradation is an increasing problem in Turkey, especially in the Middle Anatolia region where the annual precipitation is approximately 300 mm, resulting from conventional farming methods. To address this issue, the artificial neural networks (ANNs) are used, as they are flexible mathematical tools that capture data. This study aims to investigate the relationships between dust emission (PM10) and the mean weight diameter, shear stress, and stubble amount of the soil, which were measured in eight different tillage practices (conventional tillage, six types of reduced tillage, and direct seeding). The results show that the mean weight diameter, shear stress, and stubble amount of the soil varied between 4.89 and 14.17 mm, 0.40–1.23 N·cm−2, and 30.5–158 g·m−2, respectively, depending on the type of tillage works. Additionally, dust emissions generated during different tillage applications ranged from 27.73 to 153.45 mg·m−3. The horizontal shaft rototiller produced the highest dust emission, approximately 150% higher than those of disc harrow and winged chisel plows. The impact of tillage practices on dust emission was statistically significant (p < 0.01). A sophisticated 3-(7-7)-1 ANNs model using a backpropagation learning algorithm was developed to predict the concentration of dust, which outperformed the traditional statistical models. The model was based on the values of mean weight diameter, shear stress, and stubble amount of the soil after tillage. The best result was obtained from the ANN model among the polynomial and ANN models. In the ANN model, the coefficient of determination, root mean square error, and mean error were found to be 0.98, 6.70, and 6.11%, respectively. This study demonstrated the effectiveness of ANNs in predicting the levels of dust concentration based on soil tillage data, and it highlighted the importance of adopting alternative tillage practices to reduce soil degradation and dust emissions.