We selected miRNAs that have already shown association with laryngeal cancer. For each miRNA, we generated sequence-based attributes, predicted target genes, and predicted related pathways and used attribute selection to narrow which characteristics are most important for cancer-related miRNAs. We then used the data to train different ML models and found that the HT classifier performed the best. The HTbased model can predict LC using miRNAs analysis data with 86.8% accuracy and was also validated with the independent patients' data showing 83% accuracy. We identified new miRNAs that had not previously shown biological relationship with laryngeal cancer, and we found out which pathways and target genes are predicted to trigger cancer proliferation. Our methodology can be applied to other diseases in the future and the data found can be crucial in providing diagnosis or identifying targets for cancer proliferation.