Flaky tests disrupt software development pipelines by producing inconsistent results, undermining reliability and efficiency. This paper introduces a hybrid framework for adaptive test healing, combining Large Language Models (LLMs) like GPT with Reinforcement Learning (RL) to address test flakiness dynamically. LLMs analyze test logs to classify failures and extract contextual insights, while the RL agent learns optimal strategies for test retries, parameter tuning, and environment resets. Experimental results demonstrate the framework’s effectiveness in reducing flakiness and improving CI/CD pipeline stability, outperforming traditional approaches. This work paves the way for scalable, intelligent test automation in dynamic development environments.