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In this paper we introduce and evaluate a novel machine learning based approach to reduce the complexity of Intra macroblock (MB) coding. The proposed approach is based on the hypothesis that MB coding mode decisions in H.264/AVC video have a correlation with the intensities of adjacent MBs and sub-MBs. This paper also discusses and analyzes different approaches of using machine learning in Intra prediction. We discuss, amongst other features, slices, Intra prediction scheme for H.264 and data mining. We use data mining algorithms to develop decision trees for H.264 coding mode decisions. The proposed approach reduces the H.264/AVC MB mode computation process into a decision tree lookup with very low complexity. The proposed algorithm is implemented in reference software by modifying the source code and is compared with the JM reference software for H.264/AVC.