Email Spam Detection: Comparative Review of Deep, Transformer, and LLM Techniques

Email remains a mission-critical medium for personal and enterprise communication—and a persistent vector for abuse. Modern spam ranges from bulk advertisements to sophisticated social engineering and phishing, creating burdens on users and infrastructure while elevating risk. Recent advancements have shifted research decisively from sparse, handengineered features to deep neural architectures and transformer-based encoders; the latest wave introduces large language models (LLMs) with zero/few-shot classification and stronger semantic understanding. This review synthesizes peer-reviewed developments across that spectrum, comparing representative approaches and highlighting consistent themes: transformers generally outperform classical baselines on content-centric tasks; deep and graph-augmented models capture longer-range and relational cues; and LLMs enable intent-aware classification but pose cost, latency, and governance challenges. We identify open problems around dataset shift, multilingual and code-mixed content, explainability, measurement realism, and production constraints. We conclude by recommending hierarchical spam classification—a two-stage pipeline that first filters spam and then semantically categorizes legitimate mail—as a practical direction to improve downstream triage and analyst productivity while retaining precision at the perimeter.