Optimized Machine Learning and Deep Learning Approaches for Email Spam Detection

A comprehensive review of spam detection techniques emphasizes the growing importance of addressing the widespread and emerging threat of email spam, which has become a major challenge. Spam emails, including malicious content like phishing links and malware, present significant security, economic, and privacy challenges to individuals and organizations. The objective of the study is to examine and evaluate modern machine learning (ML) and deep learning (DL) strategies for detecting spam more accurately and efficiently compared to earlier techniques like blacklists or content-based filtering. This paper identifies significant ML and DL methods commonly used for detecting email spam by evaluating their effectiveness and limitations. It also highlights research gaps in existing systems and suggests integrating AI technology into spam detection. Challenges discussed include false positives, computational efficiency, and the need for adaptive models to counter advanced spam tactics. The review aims to guide future studies and improve spam detection techniques.