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This project presents a plugin-based self-healing machine learning framework aimed at improving the reliability and robustness of deployed ML models in dynamic real-world environments. The framework autonomously detects, classifies, and mitigates runtime errors by continuously monitoring model behavior using reliability signals such as data drift, prediction confidence, entropy, and label consistency. Detected anomalies are semantically classified into error types including data drift, overconfidence, and label noise, enabling a policy-driven healing mechanism to select appropriate corrective actions such as safe retraining or protective blocking. Safety guards and validation checks ensure that learning and adaptation occur only under reliable conditions, preventing harmful self-updates. Through a gated feedback loop and selective learning strategy, the framework maintains long-term model stability while reducing performance degradation. Its modular, plugin- based design allows seamless integration with existing machine learning models without modifying core model logic, thereby minimizing human intervention and providing a practical approach to fault-tolerant machine learning systems suitable for real-world deployment.