This work focused on predicting the uptake of fluoride ions from aqueous solution using polyaniline chloride-jute (PANI-Cl-Jute) as an adsorbent, in which artificial neural networks (ANNs) were used to describe the adsorption process. The breakthrough curves were analyzed using the operating conditions of the column such as flow rate, bed depth, and initial fluoride concentration. It was found that fluoride adsorption is enhanced by increasing bed height, reducing initial fluoride concentration, and reducing flow rate. Levenberg–Marquardt (LM) and Bayesian Regularization (BR) which are ANN backpropagation algorithms were tested to simulate fluoride removal efficiency from the aqueous solution. The BR algorithm gives more accurate predictions than the LM because of the low mean squared error (MSE) value of 0.000089 (BR) compared to the MSE value of 0.00079 (LM). It was found from the diagnostic analysis that both ANN algorithms achieved high prediction accuracy, with Bayesian Regularization (R2 = 0.99787) slightly outperforming Levenberg–Marquardt (R2 = 0.99676) in removal prediction efficiency. These findings could provide valuable insights into improving ANN-based algorithms for the efficient removal of fluoride ions from contaminated water.