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Document classification using supervised machine learning is now widely used on the internet and in digital libraries. Several studies have focused on English-language document classification. However, Arabic text includes high variation in its morphology, which leads to high extracted features and increases the dimensionality of the classification task. Towards reducing the curse of dimension in Arabic text classification, a wrapper feature selection method is proposed in this study. In more detail, a hybrid metaheuristic model based on the Wind Driven and Simulated Annealing is designed to solve FS task in Arabic text, known as WDFS. The Wind Driven method is initially introduced to optimize the Fs task in the exploration phase. Then, WD is hybridized with simulated annealing as a local search in the exploitation phase to enhance the solutions located by the WD. Three classifiers are utilized to evaluate the selected features using the proposed WDFS: K-nearest Neighbor, Naïve Bayesian, and Decision Tree. The proposed WDFS method was assessed on selected four groups of files from a benchmark TREC Arabic text newswire dataset. Comparative results showed that the WDFS method outperforms other existing Arabic text classification methods in term of the accuracy. The obtained results reveal the high potentiality of WDFS in reliably searching the feature space to obtain the optimal combination of features.