The protein secondary structure prediction is one of the first and most important issues for almost a quarter of century to construct the effective tool with the highest accuracy. In this present study we predicted the secondary structure of the trnP tRNA through machine learning approach that is m-fold (through mfold server). In this investigation, we also predicted the thermodynamics of the structure with secondary structure prediction. This gene structural and functional prediction will play a major role in drug designing or synthetic vaccine development or in the disease better understanding.