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Email Remains A Primary Vector For Unsolicited And Malicious Content, Motivating Robust Spam Detection At Scale. This Comprehensive Review Synthesizes Progress In Machine-learning–based Spam Classification Across Feature Engineering, Model Design, Evaluation, And Deployment. We Contrast Classical Pipelines—tokenization, N-grams, TF-IDF, And Metadata Cues—with Representation Learning Via Word/character Embeddings And Contextual Language Models. Algorithms Surveyed Span Naïve Bayes, Logistic Regression, SVMs, K-NN, Decision Trees And Random Forests, Gradient Boosting, And Deep Architectures (CNN/RNN Hybrids, Attention Mechanisms, And Transformer-based Models), Including Ensemble And Hybrid Systems That Balance Precision And Recall Under Class Imbalance. We Examine Benchmark Datasets And Evaluation Protocols, Highlighting Metric Selection (precision/recall, F1, ROC-AUC, PR-AUC), Cost-sensitive Learning, And The Impact Of Concept Drift And Adversarial Obfuscation. Practical Considerations Include Multilingual Handling, Image/URL Attachments, Header/network Features, Interpretability, Resource Constraints For On-device Filtering, And Privacy-preserving Training. The Review Identifies Open Challenges—rapidly Evolving Spam Tactics, Domain Shift, Limited Labeled Data, And Explainability—and Outlines Promising Directions: Semi/self-supervised And Active Learning, Transfer From Large Language Models, Adaptive Online Learning For Drift, Graph And URL Reputation Features, And Privacy-aware Federated Approaches. We Conclude With A Taxonomy Of Methods And Deployment Patterns, Offering Guidance On Algorithm Selection And Research Gaps For Resilient, Scalable Email Spam Filtering.