The present study delves into optimizing fluoride removal using activated alumina through the combined application of Response Surface Methodology (RSM) and Support Vector Machine (SVM). Excessive fluoride in water poses health risks, necessitating efficient removal methods. Activated alumina’s potential as a fluoride adsorbent prompts this research to enhance its efficacy using advanced modelling techniques. Systematically varying parameters like contact time, stirring rate, and alumina dosage, RSM optimizes conditions for maximum fluoride removal. The SVM model captures complex interactions, providing a robust understanding. High accuracy in predicting adsorption efficiency for training and testing datasets showcases SVM’s strength. Regression analysis yields a second-order model with adjusted and predicted R2 values of 0.9825 and 0.9321, respectively. This synergistic approach not only boosts fluoride removal but also unravels intricate variable interplay. As communities grapple with water quality, this research marks a significant stride in developing sustainable purification strategies.