Abstract: A real-time voice fraud detection system for telecommunications comprises three parallel analysis modules operating simultaneously on incoming call audio. A speech recognition module analyzes linguistic content and conversational patterns to generate a first risk score. An acoustic analysis module examines audio waveform characteristics to detect synthetic or artificially generated voices, producing a second risk score. A metadata analysis module evaluates call origination data and network information to generate a third risk score. A decision engine integrates the three risk scores using a hybrid approach combining artificial intelligence algorithms and configurable rule-based logic to determine call legitimacy. The system features adaptive learning capabilities that enable automatic updates to detection parameters based on newly identified fraud patterns without requiring complete model retraining.