Protein secondary structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry. In genome analysis, secondary structure prediction can be used to predict some aspects of protein functions (classify proteins, identify domains, annotate sequences) and recognize functional motifs. In this paper, we represent protein secondary structure as a mathematical model. To extract and predict the protein secondary structure from the primary structure, we require a set of parameters. These parameters specify any constants appearing in the model and provide a mechanism for efficient and accurate use of data. The Expectation Maximization Algorithm is used to estimate the model parameters with the use of protein datasets like RS126 by using the Bayesian Probabilistic method (data set being categorical). The ultimate objective will be to obtain greater accuracy better than the previously achieved. The future work can be extended by comparing the efficiency of EM algorithm to the other algorithms for estimating the model parameters provides a scope to use these parameters for predicting secondary structure of proteins using machine learning techniques like neural networks and fuzzy logic.