Machine learning threat detection uses anomaly detection, behavioral modeling, and predictive analytics to identify cyber threats in real time. ML models analyze logs, network traffic, and user activity to detect ransomware, phishing, and insider threats with up to 95% accuracy. Machine learning threat detection, like Debut Infotech, builds advanced detection engines featuring high-speed data processing, automated response workflows, and continuous model retraining. Examples include intrusion-detection systems, fraud analytics, endpoint monitoring, and malware classification. Differentiators include explainable AI, scalable architectures, and integration with SIEM/SOAR platforms. As cyberattacks rise globally, ML-powered detection helps organizations strengthen their security posture, reduce false positives, and respond proactively to evolving threats.