Intelligent Spam Call Classification Using Machine Learning

The widespread adoption of automated telephony and Voice over Internet Protocol (VoIP) services has generated an unprecedented surge in malicious call traffic, placing mobile users at risk of financial exploitation and privacy violations. Conventional call-screening tools, which rely on static, number-based blacklist databases, are structurally incapable of countering contemporary threats such as AI-synthesized voice impersonation (deepfakes) and Caller ID spoofing. This study introduces a novel real-time machine learning framework designed for automated spam call detection, functioning by concurrently evaluating the network-level provenance of an incoming call and the acoustic characteristics of the transmitted voice payload. The framework is realized as a native Android application built on a Kotlin Coroutine concurrency model, enabling simultaneous lookups across multiple global spam repositories combined with on-device STIR/SHAKEN protocol validation. When a call is flagged as suspicious, the corresponding audio stream is forwarded to a Python FastAPI backend, where 40 Mel-Frequency Cepstral Coefficients (MFCCs) are computed. A PyTorch neural network classifies the audio as either authentic or synthetic, achieving an accuracy of 94.2%. The system provides a scalable and efficient solution for modern telecommunication threats.