Accurate estimation of enzyme kinetic parameters such as KM and vmax from the experimental datasets is critical to characterize any enzyme. The validity of widely used standard quasi steady state approximation over decades relies on a priori information of KM and other kinetic parameters which are generally not available for newly identified enzyme systems. Progress curve analysis of the time course datasets was proposed to alleviate such issues. Since steady state and progress curve analysis use different types of datasets, the estimated KM values will be inconsistent among these methods. We show that the error in the estimation of KM using steady state methods is strongly dependent on the total reaction time. Progress curve methods work well at the timescale regime with maximum change in the curvature of the trajectory of substrate evolution. Whereas, steady state methods work across the timescales with minimum change in the curvature of the trajectory of product evolution. We propose an integrated approach which comprises of progress curve and three different steady state methods using the same time course dataset that also consider the total reaction time. We demonstrate that there exists an optimum reaction time at which all the steady state and progress curve methods show minimum possible error in the estimation of KM. We define the reliability index as the coefficient of variation of the median KM values obtained from progress and various steady state methods using the same datasets. Accurate estimate of KM can be obtained by iterating the total reaction time towards the least possible reliability index.