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Spike sorting of electrophysiological data plays an important role in deciphering useful information from the brain. Unsupervised clustering of brain data relative to respective neurons is important to understand single cell and networks dynamics. A large number of clustering techniques exist in the literature; however, the dependency of these clustering algorithms on the selection of appropriate parameters, such as, bandwidth or threshold window size is critical. Iterative methods are generally employed to estimate optimal parameters, however, significant computational time and associated large number of iterations make the clustering inefficient to implement. To address this issue, we introduce a robust Optimal Parameter Estimation (OPE) Algorithm that can estimate the optimized parameters in a fast and efficient way. The performance of the OPE algorithm is tested on MeanShift and DBSCAN clustering algorithms …