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Wireless mesh networks (WMNs) have experienced an enormous growth over the past few years. The performance of WMNs depends on the joint effect of both routing algorithms and rate adaptive algorithms. The performance of various routing algorithms has been studied extensively in the literature. However, little work has been done to evaluate the cross-layer impact of rate adaptive algorithms in WMN environments. In this paper, we compare the performance of several rate adaptive algorithms to exploit the multi-hop performance in WMN environments. In addition, a novel rate adaptive algorithm is proposed via the machine learning approach to robustly reflect the channel information. The goal of our design is to maximize the spectral efficiency. Through extensive computer simulations under different channel and topology environments, experimental results demonstrate the proposed algorithm outperforms other existing algorithms in WMN environments.