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Phishing and spam are examples of unsolicited emails, result in significant financial losses for businesses and individuals every year. Numerous methodologies and strategies have been devised for the automated identification of spam, yet they have not demonstrated complete predictive precision. Within the spectrum of suggested methodologies, ML and DL algorithms have shown the most promising results. This article scrutinizes the outcomes of assessing the efficacy of three transformation-based models - BERT, AlBERT, and RoBERTa - in scrutinizing both textual and numerical data. The proposed models achieved higher accuracy and efficiency in classification tasks, which was a notable improvement above traditional models such as KNN, NB, BiLSTM, and LSTM. Interestingly, in several criteria the Roberta model achieved almost perfect accuracy, suggesting that it is very flexible on a variety of datasets. [ABSTRACT FROM AUTHOR]