The tiling transformation is one of the most crucial code optimization techniques to expose data locality and parallelism. The main idea is to split the initial iteration space into blocks and traverse them in a special order. This transformation is parametric and very sensitive to parameter tuning. Poor parameter tuning can lead to much lower performance than the initial code. Existing state-of-the-art solutions consider a restricted list of parameters to handle this issue and guarantee safe solutions. Our work proposes solutions that go beyond current state-of-the-art techniques and gain additional speedup considering a larger set of options for tiling. Our approach is based on Machine Learning methods and automatically derives heuristics to tune tiling parameters. We can predict: 1) the optimal partitioning matrix of the iteration space 2) the tile sizes, 3) the optimal directions for scanning inter-tiles, 4) the optimal directions for scanning intra-tile elements. The optimal selection of these parameters is crucial especially for programs that have data dependencies. We introduce sets of features that feed our models in their predictions. The first set encodes data dependencies, the second one captures the level of parallelism and data locality in a code. The third one aggregates information about the iteration space. Our approach surpasses existing feature spaces for tiling parameters prediction. Moreover, it could be used in conjunction with auto-tuners to iterate through the iterative search.