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Background: Wastewater from the mineral fertilizer production, agribusiness containing ammonium ions causes significant harm to fish farming; therefore, it must be purified before discharge. Ion-exchange sorption is a promising method for isolating ammonium cations. The object of the study was a chemisorption fiber VION KN-1, which has developed surface and high sorption rate. Purpose: To study the sorption kinetics of ammonium cations from aqueous solutions on VION KN-1; to train an ANN to predict the degree of recovery of ammonium ions from wastewater using Statistica Neural Networks Version 13. Methods: The ammonium ion concentration in the solution was established by direct potentiometry. Sorption isotherms were constructed using the method of variable concentrations. To determine the limiting stage, the obtained kinetic dependencies were represented in the coordinates of the Boyd-Adamson equations for internal/external diffusion. Results and Discussion: During sorption from solutions with different ammonium nitrogen contents, the values of distribution coefficients (Kd) are at the level of 2.3ꞏ103 cm3/g, which significantly exceeds this parameter for granular ionites. Experimental sorption data were verified using Freundlich (R2 = 0.9224) and Langmuir (R2 = 0.9996) isotherms. The maximum degree of recovery (over 96 %) was achieved by passing a solution with a concentration of 11.3 mmol/dm3. Using an array of experimental data, the MLP-3-5-1 neural network was trained. The coefficient of determination R2 = 0.999420 obtained for the training sample characterizes high network performance. Conclusions: The Langmuir equation better describes the process of NH4+ sorption on a fibrous sorbent. It is reasonable to use VION KN-1 at the fine treatment stage. Ammonium ion desorption from the fiber was performed by acid solution. The resulting solutions of ammonium salts can be used as liquid fertilizers. The trained neural networks can be used to predict the degree of recovery of ammonium ions by sorbent VION KN-1.