Critical Clearing Time (CCT) estimation in real-time is considered a challenging computing problem. It could be determined by calculating the Critical Clearing Angle (CCA) using Equal Area Criterion (EAC) followed by time domain simulation. This method is used for off-line simulation and is not adequate for real-time estimation due to the wide range of power system operating conditions. The estimation of the CCT in real time was also attempted using an energy function, but the method did not achieve the desired accuracy. This paper focuses on the development of a technique for estimating CCT in realtime using Adaptive Neuro Fuzzy Inference Systems (ANFIS) and Synchro-Phasors Measurement Units (SPMUs). The technique can be used for transient stability assessment and control in real-time under all operating conditions. The method is tested on a Single Machine to Infinite Bus (SMIB) test system. The paper also presents a comparative study for CCT estimation using ANFIS, Artificial Neural Networks (ANN), or the exact Time Domain Equal Area Criterion (TDEAC). The results of the research confirm the effectiveness of the developed ANFIS over other techniques for real-time estimation of CCT.