<p>Bioaugmentation of soils with selected microorganisms during phytoextraction can be the key solution for successful bioremediation and should be accurately calculated for different physicochemical soil properties and heavy metal availability to guarantee the universality of this method. Equally important is the development of an accurate prediction tool to manage phytoremediation process. The main objective of this study was to evaluate the role of three metallotolerant siderophore-producing <i>Streptomyces</i> sp. B1–B3 strains in the phytoremediation of heavy metals with the use of <i>S. dasyclados</i> L. growing in four metalliferrous soils as well as modeling the efficiency of this process based on physicochemical and microbiological properties of the soils using artificial neural network (ANN) analysis. The bacterial inoculation of plants significantly stimulated plant biomass and reduced oxidative stress. Moreover, the bacteria affected the speciation of heavy metals and finally their mobility, thereby enhancing the uptake and bioaccumulation of Zn, Cd, and Pb in the biomass. The best capacity for phytoextraction was noted for strain B1, which had the highest siderophore secretion ability. Finally, ANN model permitted to predict efficiency of phytoextraction based on both the physicochemical properties of the soils and the activity of the soil microbiota with high precision.</p
