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The rise in spam calls—both audio and video—poses significant risks to users. Recent studies indicate a dramatic increase in AI-generated voices and deepfake videos designed to deceive and compromise security. AI-driven impersonation attacks account for losses of over $10 billion annually, and nearly 60% of victims are deceived by highly realistic synthetic voices or videos. This paper presents a novel AI-powered framework for real-time spam detection and prevention. Our system combines Natural Language Processing (NLP) and deep learning techniques to analyze conversational content and context, detecting suspicious behaviors such as requests for sensitive information including gift card payments or PIN numbers. These insights trigger runtime alerts that protect users from fraudulent interactions.To further enhance security, we introduce advanced methods for detecting machine-generated content and verifying human liveness. Techniques such as anomaly detection for voice modulation, deepfake video frame analysis, and semantic evaluation of conversational patterns help differentiate authentic human interactions from AI-generated impersonations. Additionally, the system leverages scalable distributed AI models and Specialized Language Models (SLMs) optimized for conversational intent and media integrity analysis. This distributed approach ensures real-time detection with minimal latency, making it suitable for large-scale deployments across diverse communication platforms.This paper elaborates on the system architecture, the role of distributed AI, and the underlying techniques used to enable this solution. Experimental evaluations demonstrate the efficacy of our approach in identifying and mitigating evolving spam threats while maintaining performance at scale.