Email spoofing and spam detection remain critical challenges in cybersecurity, with email being the most exploited vector for phishing and fraud. This paper explores state-of-the-art methods for identifying spoofing and spam threats, including the use of machine learning (ML), deep learning (DL), and natural language processing (NLP). By examining email header components, content analysis, and authentication protocols such as SPF, DKIM, and DMARC, we highlight existing vulnerabilities and propose integrated detection systems. Key advancements include hybrid approaches combining rule-based and ML techniques, dynamic feature selection, and leveraging fuzzy hashing tools for detecting malicious patterns. Despite significant progress, challenges such as evolving attack strategies and dataset limitations persist. This research underscores the necessity for adaptive, scalable, and holistic detection frameworks to combat the increasing sophistication of email-based threats effectively.