Stylometric features embedding is crucial for author attribution, using statistical and linguistic patterns to identify unique stylistic traits of writers. This method focuses on word length, sentence complexity, and punctuation for comprehensive authorship profiles. Analyzing these features reveals patterns in individual writing styles, enhancing attribution accuracy. This study evaluates various classification algorithms and feature extraction techniques to distinguish texts by the same author from those by others. It faces challenges due to nuanced stylistic differences, text length inconsistencies, genre diversity, and contextual variations. The study employs TFIDF for feature extraction, assessing word importance in documents. Results show TFIDF effectively captures authorship characteristics, with non-linear Support Vector Machines performing strongly across feature dimensions. Optimizing SVM parameters through grid search improves performance with larger feature sizes, achieving peak accuracy of 81.13%. These findings underline the importance of parameter optimization in enhancing SVM capabilities, while integrating Word2Vec embeddings boosts classification accuracy, with Random Forest proving most effective.