Reverse osmosis and distillation (with or without membrane) are the major conventional desalination techniques that use huge solar or electrical energy. Such energy-intensive desalination techniques are expensive and unsustainable. Therefore, a sustainable and ecofriendly desalination technique is essential to minimize the adverse impacts on the environment. Microbial Desalination Cell (MDC) is a green and low-cost alternative desalination method that uses bacteria as a tool to generate electricity from the biodegradation of organics present in wastewater. The biologically produced cell potential and electricity drive the ions (mostly Na+ and Cl- ) transport across the membranes to achieve seawater desalination. Such a microbial desalination technique is a completely eco-friendly and green technology as it requires no external energy input. Recently, few studies have been done on the MDC reactor design, development of MDC electrode and membrane materials, optimizing the operational conditions, and enhancing the desalination and waste removal performances. However, limited studies have been done to develop the mathematical models of MDC system for future performance and real application prediction. Therefore, this study aims to develop a Support Vector Regression (SVR) mathematical model to predict the MDC performance with time under various operation conditions using MATLAB as a tool. The model has been validated by a bench-scale experimental demonstration of MDC in the Water and Wastewater laboratory at Sultan Qaboos University, Muscat, Sultanate of Oman. The parameters contributing to MDC system performance were used as inputs for proper validation, including external resistance (Rext), time (t), electrical conductivity (EC) and pH of anode, cathode and desalination chambers, and biogenerated current flow (I). Two types of MDC, Air Pumped Microbial Desalination Cell (APMDC) and Air Cathode Microbial Desalination Cell (ACMDC) were used for validation purposes. The salt removal performances obtained from the experiment were used as an output variable. The SVR models were trained and cross-validated, and the sensitivity analysis was employed to evaluate and state model output. The kernel function polynomial, radial basis function (rbf) and Gaussian kernels were vi selected and optimized for the developed SVR model based on the set of data and model purpose. The statistical errors, including coefficient of determination (R2 ), correlation, mean absolute relative error (MARE) were utilized to measure the prediction accuracy and model performance. The sensitivity analysis of different functions showed that the polynomial achieved the lowest MARE at 5.58% with 0.99 for both coefficients of determination Rand correlation, compared to the other kernel functions of Gassuian and radial basis function (Rbs). The cross-validation results confirmed the efficiency of processing the SVR model with results of 8.37% MARE and 0.99 for both R2 and correlation. The simulation of experiential data with SVR model emphasized that the ACMDC attained 99% salt removal in 5 days. While under the same conditions, the APMDC obtained 99% salt removal in 17 days. The MDC mathematical SVR model will be helpful in determining the optimal operating conditions of microbial desalination systems, providing a better understanding of MDC behavior, predicting larger-scale systems, and avoiding upcoming system failures.