Tick-associated viruses remain a substantial zoonotic risk worldwide, so knowledge of the diversity of tick viruses has potential health consequences. Despite their importance, large amounts of sequences in public datasets from tick meta-genomic and –transcriptomic projects remain unannotated, sequence data that could contain undocumented viruses. Through data mining and bioinformatic analysis of more than 37,800 public meta-genomic and -transcriptomic datasets, we found 83 unannotated contigs exhibiting high identity with known tick viruses. These putative viral contigs were classified into three RNA viral families (Alphatetraviridae, Orthomyxoviridae, Chuviridae) and one DNA viral family (Asfaviridae). After manual checking of quality and dissimilarity toward other sequences in the dataset, these 83 contigs were reduced to five contigs in the Alphatetraviridae from four putative viruses, four in the Orthomyxoviridae from two putative viruses, and one in the Chuviridae which clustered with known tick-associated viruses, forming a separate clade within the viral families. We further attempted to assess which previously known tick viruses likely represent zoonotic risks and thus deserve further investigation. We ranked the human infection potential of 133 known tick-associated viruses using a genome composition-based machine learning model. We found five high-risk tick-associated viruses (Langat virus, Lonestar tick chuvirus 1, Grotenhout virus, Taggert virus, and Johnston Atoll virus) that have not been known to infect human and two viral families (Nairoviridae and Phenuiviridae) that contain a large proportion of potential zoonotic tick-associated viruses. This adds to the knowledge of tick virus diversity and highlights the importance of surveillance of newly emerging tick-associated diseases.