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Wireless sensor networks are the decentralized type of networks which sense data and pass this information to base station. The network is deployed on faraway places and due to the small size of the sensor nodes energy consumption, quality of service are some of the major issues of the network. In nutshell, sensor nodes have to perform sensing, data processing and communication in an isolate physical area. As battery is fixed and cannot be replaced, there must be best energy efficient routing algorithm required to be implemented in every sensor network to increase the network lifetime manifolds. The improved adaptive modulation technique will modulate the low frequency data into high frequency which leads to the reduction in bit error rate. In proposed algorithm, the SVM classifier is applied which will classify the data into two classes. For regression, classification as well as general pattern recognition, the SVM classifier is proposed. There are many solutions proposed for the same. But in this work, the adaptive modulation scheme is proposed that is based on PCA algorithm which selects best modulation signal to increase frequency of the data. Both, proposed and existing algorithms are implemented in MATLAB and results are analyzed in terms of various parameters.