The alternative solution for predicting yields involves leveraging the latest technological advancements in unmanned aerial vehicles (UAVs) imagery. This approach aims to empower agricultural systems operating in nature-dependent conditions, enabling them to optimize the utilization of limited resources effectively. This study aimed to provide the agricultural data management needed for the adoption of sustainable agriculture systems and the transition of precision to smart agriculture systems by combining technology. The study was carried out in the 2019–2020 vegetation period to determine the effect of the parameters on the yield in the areas where organic farming is grown in Isparta oil rose (Rosa Damascena Mill.) and to develop yield prediction models by combining them with spectral measurements. In the study, the phenological, morphological, and biochemical changes of the Isparta oil rose were examined periodically, and the effects of biotic and abiotic stress factors during the vegetation period were determined in the study and testing area. For the early yield prediction of organic Isparta oil rose was chosen thirteen parameters as soil moisture, pH, electrical conductivity (EC), soil temperature, and nine vegetation indices were derived from UAV-acquired multispectral images. The parameters were functioned in machine learning algorithms, which were multiple linear regression (MLR), multivariate adaptive regression splines (MARS), decision trees (chi-squared automatic interaction detector (CHAID), exhaustive chi-squared automatic interaction detector (ExCHAID), classification and regression tree (CART)), random forest (RF)) and artificial neural network (ANN), to develop the prediction models. Early period (day 69) for organic rose yield prediction models were determined to be useable as MARS 1 (0.907 R2), ExCHAID 1 (0.888 R2), CART 1 (0.931 R2), and RF 1 (0.909 R2). As a result of the study, the models can be used integrated with UAV and soil data for the early prediction of Isparta oil rose yield under organic farming systems, especially during the Agriculture 4.0 transition phase in our country. It is such that models be developed and used in different ecologies for sustainable agricultural systems.