Countries and areas in Asia and Oceania have suffered from large earthquake and tsunami disasters. Scientists, Researchers, Academicians and soon have tried lots of different ways of predicting Earthquakes, but successful to some extent only. Most of them have a superior idea of where an Earthquake is most likely occurs, but they still unable to tell exactly when an earthquake will occurs. It would be better to detect exactly when Earthquakes will occur. Detection of Earthquake was done earlier based on W-MLP and MLP, Wavelet-Aggregated Signal and Synchronous Peaked Fluctuations model, detection using the P waves of the Earthquake, prediction based on radon emissions, EEW algorithm, M8 algorithm, prediction using extraction of instantaneous frequency from underground water, but neither of them could provide an effective and efficient result. In the present research, seismic signals are analyzed by using HAAR, DB, SYM, COIF and BIOR wavelet transforms in order to evaluate the energy, frequency, magnitude of the signal and the results are compared with each other. The minor quakes are neglected and the surface wave magnitude of the quakes that show impact on earth’s surface is calculated and found as 3.0. The obtained results from HAAR, DB, SYM, COIF and BIOR wavelet transforms are taken up as datasets and are tested using classification algorithms such as J48, Random Forest, REP tree and LMT to evaluate the accuracy, precision and recall performance measures.