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Continuous changing in house prices very rapid and observed in last years which depends on many factors like position, area, population in the city and another requirement for individual house price, there is a lot of articles studied and adopted traditional ways for machine learning in house price prediction accurately but few of them focused on individual performance for complicated models. To explore effected factors in prediction way in this paper we applied traditional machine learning theory to investigate many models due to many limited we take some of techniques like (random forest, Extreme Gradient Boosting and stacked generalization model) and applied on several models to obtain more accurate results for house price prediction. We take Baghdad as a sample for house price and impact factor on difference cities, services, population, area, building age and others we mentioned in our paper by taking and applying machine learning house price prediction. [ABSTRACT FROM AUTHOR]