<p>Uneven water distribution in sprinkler irrigation systems, especially due to wind variability and different land slopes, is one of the main problems that reduce irrigation efficiency. In this study, an intelligent approach based <br /> on the integration of GIS (geoinformation systems), machine learning (ML), and deep learning (DL) technologies was developed to solve this problem. In the study, zones were classified into “dry”, “normal”, or “wet” based on zonal parameters such as Slope, Aspect, wind speed and direction, evapotranspiration (ET), and NDVI. An initial classification accuracy of 47.5% was achieved using the Random Forest model. Then, a 1D convolutional neural network (CNN) model was used, to learn learn each zone based on 6 parameters. The CNN model achieved a training accuracy of more than 80% and a validation accuracy of about 75% over 50 epochs. This approach serves as a strong basis for creating an accurate and optimized sprinkler irrigation system under the conditions of climate change and complex terrain in Uzbekistan and Central Asia.</p>