Existing fluoride removal systems are not effective due to the high treatment time and operational cost which addresses an urgent need in developing an effective alternative fluoride removal system. Scanty research was reported using Microalgae sp. as a potential natural biosorbent agent in removing fluoride from the ground water and no research was documented on applying RSM and ANN as a mathematical tools to optimize the Biosorption process. With these lacunae, the present study aimed in optimization of the biosorption process of Microalgae SP. for enhancing fluoride removal efficiencies applying Response Surface Methodology (RSM) and Artificial Neural Network (ANN). In the present study, initially four parameters of the Microalgae sp. biosorption process such as pH, contact time, sorbent dosage, and agitation were optimized using CCD-RSM. Further optimized value of the four factors with respect to end fluoride content was compared and validated using ANN methodology. The optimized condition of the present study i.e (7 pH, Three days of contact time, 1.75ml sorbent dosage and 150 rpm agitation speed) was resulted in reduction of fluoride from initial content of 2mg/l to the final content of 0.55mg/l. ANN has reported the error values of (1.0) for the optimized trail of the CCD-RSM design. The present study concluded by providing validated optimized design reporting approx 4 fold (0.55 mg/ml) reduction in fluoride content with respect to initial fluoride content (2.0 mg/ml). The pilot plant (200 L) scale-up studies for the reported optimized design are under investigation.