This paper presents an effective way to enhance the secret key guessing ratio in machine learning based power analysis attack on secure systems such as smartcards. The power supply current traces are obtained by varying the atmospheric temperature for all possible values of key. The collected power supply current traces are then pre-processed by using wavelet transform, data normalization and principal component analysis (PCA) and the featured data samples are used to train the probabilistic neural network (PNN). The network is then tested with a current trace obtained from the device under attack and the correct key is identified. The proposed method achieves 100% success rate in guessing the secret key of the cryptographic algorithm with minimum number of power traces when compared to the existing methods of machine learning technique.