In modern software engineering, ensuring quality and reliability under rapid release cycles requires intelligent and adaptive testing strategies. Traditional automation remains limited in scalability and predictive capability, while recent AI-based approaches often treat test generation and fault prediction separately. This paper proposes a Hybrid AI Framework that integrates Large Language Models (LLMs) for context-aware automated test generation with Machine Learning (ML) models for predictive fault detection. The framework combines focal method analysis and risk-based prioritization to enhance coverage and defect localization. Using open-source datasets (Defects4J and Methods2Test), a prototype implementation based on LLaMA-2-7B-Chat and XGBoost achieved +12.9 pp coverage (+18.5% relative) and +0.07 recall (+9.1%) over ChatUniTest, and TESTPILOT. The proposed architecture demonstrates how generation and prediction can operate synergistically within continuous integration pipelines, reducing manual effort while improving adaptability to code changes. This study contributes an architectural blueprint and validation workflow for AI-driven quality assurance, paving the way toward scalable, explainable, and hybrid intelligent testing systems.