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We delve deep into modern content-based e-mail spam filtering methods, focusing on machine learning-driven filters and their adaptations. Our discussion covers key concepts, methods, notable advancements, and recent innovations in this field. Initial analysis reveals the basics of e-mail spam filtering and the role of feature engineering. We conclude by exploring techniques, methodologies, evaluation criteria, and insights from recent progress, paving the way for future research. In knowledge engineering approach the hard and fast rule is specifying a set of principles according to which email is classified as spam or ham. Application of this method, doesnt shows any promising results because the rules should be necessary. Constantly updating the rules and methods just causes waste of time and requires more maintenance. As compared to knowledge Engineering, Machine learning is more appropriate approach. It does not have to specify any rules. A set of pre-classified e-mail messages is used here in place of set of rules. Machine learning approaches have a wide range of Importance and a lot of algorithms can be used for e-mail filtering and classification. These include Support Vector Machine, Nave Bayes.

Copyright © 2024 ANIL SABHAJEET YADAV. This is an open access article distributed under the Creative Commons Attribution License.