Developing an AI-driven spam filtering system capable of detecting spam communications and defeating attackers’ complex evasion and obfuscation strategies is the primary objective of this research. To circumvent common screening systems, modern spammers employ intentional misspellings, odd characters, concealed text, image-based spam, and word replacements. Instead of merely searching for keywords, the suggested Paper combines artificial intelligence, machine learning, and natural language processing to identify spam through behaviour analysis, pattern identification, and context understanding. This method increases the model’s adaptability and accuracy in the real world by training it on datasets that include both legitimate and fraudulent spam messages. The project aims to improve spam detection, reduce false positives, and provide a robust, flexible, and intelligent screening system to safeguard contemporary email and messaging applications from emerging spam threats.