A model has been developed for the joint effect of multicrystalline silicon (mc-Si) wafer characteristics and diffusion parameters on minority carrier lifetime. Numerical simulations of mc-Si lifetime based on drift-diffusion model involve many simplifying assumptions, leading to non-practical results for optimizing gettering process. To overcome this deficiency, the proposed model has a completely different approach to estimate carrier lifetime enhancement for various mc-Si wafers processed under different diffusion conditions. SVM regression, which is a machine learning-based procedure, has been employed to foretell the efficiency of gettering through predicting the average lifetime after diffusion. Experimental data from processed wafers have been used to train the model. Examining test data using mean square error (MSE) confirms that Gaussian model has the best accuracy.