Accurate trajectory prediction is vital for various applications, including autonomous vehicles. However, the complexity and limited transparency of many prediction algorithms often result in black-box models, making it challenging to understand their limitations and anticipate potential failures. This further raises potential risks for systems based on these prediction models. This study introduces the Performance Monitor for Black-Box Trajectory Prediction Model (PMTM) to address this challenge. The PMTM estimates the performance of black-box trajectory prediction models online, enabling informed decision-making. The study explores various methods’ applicability to the PMTM, including anomaly detection, machine learning, deep learning, and ensemble, with specific monitors designed for each method to provide real-time output representing prediction performance. Comprehensive experiments validate the PMTM’s effectiveness, comparing different monitoring methods. Results show that the PMTM effectively achieves promising monitoring performance, particularly excelling in deep learning-based monitoring. It achieves improvement scores of 0.81 and 0.79 for average prediction error and final prediction error monitoring, respectively, outperforming previous white-box and gray-box methods. Furthermore, the PMTM’s applicability is validated on different datasets and prediction models, while ablation studies confirm the effectiveness of the proposed mechanism. Hybrid prediction experiments further demonstrate the value of the proposed monitor from an application perspective.