The incorporation for the Artificial Intelligence (AI) into software development has drastically revolutionized conventional approaches, improving efficiency, precision, and scalability. AI powered solutions for code generation, and debugging utilize sophisticated machine learning (ML), and natural language processing (NLP) methodologies to automate repetitive operations, enhancing efficiency, and elevating software quality. Utilizing pre-trained models like Codex, these technologies enable contextually pertinent code generation, and the early detection for the vulnerabilities during the Software Development Life Cycle (SDLC). This study examines the progress within AI-driven code creation, its impact upon software quality assurance, and its function across the software development life cycle (SDLC) phases. It also tackles the problems related to AI adoption, encompassing ethical issues, dependence upon data quality, and implementation expenses. The findings emphasize AI like an essential component within contemporary software engineering, fostering innovation, and revealing prospects for enhancement to optimize efficiency, and dependability within software development.