Quasicrystals are aperiodically ordered solids that exhibit long-range order without translational periodicity, bridging the gap between crystalline and amorphous materials. Due to their lack of translational periodicity, information on atomic arrangements in quasicrystals cannot be extracted by current crystalline lattice recognition softwares. This work introduces a method to automatically detect quasicrystalline atomic arrangements and tiling using image feature recognition coupled with machine learning, tailored towards quasiperiodic tilings with 8-, 10- and 12-fold rotational symmetry. Atom positions are identified using clustering of feature descriptors. Subsequent nearest-neighbor analysis and border following on the interatomic connections deliver the tiling. Support vector machines further increase the quality of the results, reaching an accuracy consistent with those reported in the literature. A statistical analysis of the results is performed. The code is now part of the open-source package AiSurf.