For cleaner and sustainable greenhouse crops production, it is essential to successfully manage the needs and resources. Thus the prediction of the greenhouse microclimate, especially the temperature and relative humidity is of great interest. The research done in this area is, however, still limited, and a number of machine learning techniques have not yet been sufficiently exploited. The objective of this paper is to evaluate two greenhouse modeling techniques (machine learning (Artificial Neural Networks (ANN), Support Vector Machine (SVM), Bagging trees (BG) and Boosting trees (BT)) and Computational Fluid Dynamics (CFD) methods and assess the impact of the seasonal changes on machine learning performances. The study was carried out in a commercial greenhouse located in Agadir, Morocco, and the experimental data were collected during October and March. Results show that all predictive models are capable of predicting the inside air temperature (Tin) and relative humidity (Rhin) of the greenhouse with a quite good precision (R>0.98, nRMSE0.98, nRMSE