Satellite imagery has revolutionised sustainable land management and crop yield forecasting. This article presents the results of the effective use of precision farming practices based on Remote Sensing (RS) data on the example of sunflower crops in 2 soil and climatic zones of Kazakhstan. Calculation of the correlation coefficient of the sunflower crop remote sensing data for further optimisation and increase of the resolution of the information of the space images was done. Through testing the classification of macronutrients in the soil (nitrogen, phosphorus, potassium, humus) using remote sensing data, this study examined two distinct soil and climate zones in Kazakhstan. The findings revealed that the integration of macroelement results in the soil, vegetation index of plant growth and development (NDVI) from the Sentinel-2 L2A satellite, Geographic Information Systems (GIS) data, and mathematical modeling leads to a 25 % reduction in fertilizer consumption. Additionally, it enhances the precision of yield forecasts by 92 %. The novelty of this study lies in the effective use of remote sensing data and precision farming techniques to optimize fertilizer consumption and sunflower yield forecasting, which can contribute to sustainable land management and increase farmers' profits.