Computational methods play an increasingly pivotal role in modern drug discovery by accelerating and streamlining compound selection. In this study, a consensus virtual screening strategy integrating machine learning (ML) and molecular docking was employed to identify potential antiviral agents from Myrtus communis L. phytochemicals against human papillomavirus (HPV). HPV, a DNA virus, is a major cause of cervical cancer and genital warts. ML classifiers trained on known HPV inhibitors predicted active myrtle compounds, followed by docking to assess binding affinities with four HPV early proteins across major variants. Five top-scoring phytochemicals-myrtucommulones A, C, and E, semimyrtucommulone, and tellimagrandin II-exhibited consistent activity across both models and showed strong stability in molecular dynamics simulations. Binding free energy analysis via MM/GBSA confirmed favorable protein–ligand interactions. These compounds, with documented antiviral and anticancer properties, are promising candidates for further experimental validation in anti-HPV drug development.