We present the calibration system for heterogeneous MOx sensor array where machine learning-based techniques, Linear Regression, Non-Linear Curve Fitting, and Artificial Neural Network, are exploited to reduce the impact from temperature and humidity. For the evaluation, we have setup the gas concentration measurement system and recorded the sensor outputs from Temperature-Cycled Operation responses of five heterogenous MOx sensors. The proposed calibration system with ANN-based calibration system shows the reduction of gas sensors variation due to temperature and humidity 73% on average, and presents maximum 92% reduction for benzene, 75% for toluene, 83% for ethylbenzene, and 91% for xylene gases, respectively.