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Flight delay is inevitable and it plays an important role in both profits and loss of the airlines. An accurate estimation of flight delay is critical for airlines because the results can be applied to increase customer satisfaction and incomes of airline agencies. There have been many researches on modeling and predicting flight delays, where most of them have been trying to predict the delay through extracting important characteristics and most related features. However, most of the proposed methods are not accurate enough because of massive volume data, dependencies and extreme number of parameters. This paper proposes a model for predicting flight delay based on Machine Learning (ML). ML is one of the newest methods employed in solving problems with high level of complexity and massive amount of data. Moreover, ML is capable to automatically extract the important features from data. Furthermore, due to the fact that most of flight delay data are noisy, a technique based on stack denoising autoencoder is designed and added to the proposed model. Also, Various algorithms like Random Forest, Decision Tree, MLP Classifier are applied to find weight and bias proper values, and finally the output has been optimized to produce high accurate results.