Phishing attacks significantly threaten individuals and organizations, resulting in substantial liabilities. This study investigates multiple machine learning and deep learning approaches to enhance phishing detection, utilizing 208,704 emails comprising 108,693 legitimate and 99,225 phishing emails. Techniques explored include SVM, RF, LR, LSTM, and Bi-LSTM. NLP techniques, such as FastText embedding and lemmatization, were utilized for email text preprocessing. The models were evaluated using performance metrics such as recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (ROC-AUC). The experimental results showed that the SVM model achieved an F1-score of 95.43%, a recall of 95.43%, and an AUC of 95.36%. The LSTM model obtained an F1-score of 91.76%, a recall of 91.77%, and an AUC of 97.16%. These findings indicate that SVM excels in precision and recall, while LSTM performs better in distinguishing between phishing and non-phishing emails, as evidenced by its higher AUC. This research contributes to cybersecurity by showcasing the effectiveness of advanced machine learning and deep learning models in enhancing phishing email detection, leading to more secure digital communication environments.