Reliable anomaly detection in satellite telemetry is critical for mission success, yet traditional threshold-based methods struggle with complex and evolving patterns. This work presents machine learning (ML) techniques to analyze high-dimensional telemetry data. Evaluations of real-world satellite telemetry datasets demonstrate the potential of ML to enhance spacecraft health monitoring and reduce manual intervention.