This study introduces a novel computational approach to authorship attribution (AA) in Arabic poetry, using the entire Classical Arabic Poetry corpus for the first time and offering a direct analysis of real cases of misattribution. AA in Arabic poetry has been a significant issue since the 9th century, particularly due to the loss of pre-Islamic poetry and the misattribution of post-Islamic works to earlier poets. While previous research has predominantly employed qualitative methods, this study uses computational techniques to address these challenges. The corpus was scraped from online sources and enriched with manually curated Date of Death (DoD) information to overcome the problematic traditional sectioning. Additionally, we applied Embedded Topic Modeling (ETM) to label each poem with its topic contributions, further enhancing the dataset’s value. An ensemble model based on CAMeLBERT was developed and tested across three dimensions: topic, number of poets, and number of training examples. After parameter optimization, the model achieved F1 scores ranging from 0.97 to 1.0. The model was also applied to four pre-Islamic misattribution cases, producing results consistent with historical and literary studies.