Abstract
The analysis of email spam detection methods is pivotal in the realm of cybersecurity. This studymeticulously explores a variety of techniques utilized for discerning spam emails, encompassing traditionalmethodologies alongside sophisticated approaches such as machine learning algorithms. The examined techniquesinclude Support Vector Machines, Naive Bayes Multinomial Classifier and Random Forest Classifier with a primaryobjective of identifying the most effective approach among them. By scrutinizing each method's strengths andweaknesses, encompassing factors like detection accuracy and computational efficiency, this research seeks to offer acomprehensive overview of their utility in combatting email spam. Additionally, the study investigates the impact ofhuman elements, such as user education and awareness initiatives, in bolstering overall detection performance. Byamalgamating insights from various scholarly endeavors, this inquiry not only enriches the understanding ofcontemporary spam detection practices but also delineates pathways for future research endeavors and informs theformulation of resilient cybersecurity protocols.
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