Branch prediction is crucial in improving the throughput of microprocessors. It reduces branching stalls in the pipeline, which helps to maintain the instruction execution flow. Of these instructions, conditional branches are non-trivial in determining the microprocessor performance and throughput. Modern microprocessors accurately predict the branches using advanced branch prediction techniques. Appropriately estimating the branch mis-predictions benefits to improve the overall performance of an application through effectively saving the CPU cycles. In general, collecting branch prediction statistics using state-of-the-art simulators is time consuming and not scalable. We present a novel Monte Carlo simulation framework that predicts branch mis-prediction rate. Our framework produces results that suggest that the mis-prediction rates on three scientific applications are similar (with an average difference of 0.3%) to that of a Markov model of a 2-bit saturating branch predictor.