A relatively new area of study, quantum machine learning, seeks to bring together the quantum computer’s significant performance improvement and the machine learning algorithms’ capacity to solve real world problems. In this research work,a quantum regression model (QRM) is proposed by combining an autoencoder and dressed quantum circuit (DQC) to predict the behavior of fiber optic temperature sensor. The autoencoder is employed to augment the experimental data-setas it is insufficient to train the DQC model. We examined the regression performance of the QRM by running multiple simulations by varying the quantum hyper-parameters such as quantum depth Qdepth, number of shots nshots andthe number of qubits nqubits of the quantum node. Moreover, the regression performance with the unknown data exhibits high R-squared score (r2scr) as0.965, high explained variance (ExpV arscr) as 0.969 and less maximum error(MaxErrscr) as 0.212 for 4 Qdepth, 1500 nshots and 4 nqubits. On the otherhand, we proved the superiority performance of the proposed QRM in predicting relative power and it is compared with four conventional machine learning regressors,namely artificial neural network (ANN) regressor, support vector regressor(SVR), decision tree (DT) regressor, and random forest (RF) regressor.