<p>This paper explores the integration of Artificial Intelligence (AI) and automation within the Behavior-Driven Development (BDD) paradigm, using the PyTest-BDD framework, to enhance Software Quality Assurance (SQA) processes. Traditional SQA methods struggle with the increasing complexity and rapid release cycles of modern software development. This research demonstrates how AI can address these challenges through intelligent test generation, prioritization, and anomaly detection. The proposed framework utilizes Natural Language Processing (NLP) to analyze requirements, machine learning (ML) to generate and prioritize test scenarios, and deep learning (DL) for anomaly detection, all within the PyTest-BDD ecosystem. This approach fosters a collaborative environment between human testers and AI agents, leading to more robust testing with reduced human overhead.The framework offers reduced human error, faster feedback loops, and increased team collaboration, thereby reducing development time and improving software reliability. AI-powered test prioritization and anomaly detection are shown to be effective in identifying subtle defects. The modular and extensible nature of the framework allows for a flexible and scalable testing system.</p>