Continuous Integration and Continuous Deployment (CI/CD) pipelines are foundational to modern software engineering, enabling accelerated software delivery, higher quality, and improved operational resilience. Despite widespread adoption, traditional CI/CD pipelines face persistent challenges, including unpredictable failures, resource bottlenecks, security vulnerabilities, and operational inefficiencies. These challenges are magnified in complex, distributed, and cloud-native environments where microservices, containerization, and dynamic scaling introduce additional layers of complexity. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) offer promising solutions to these issues by enabling predictive failure detection, performance optimization, and automated security integration. This research synthesizes contemporary literature and empirical studies on AI-driven CI/CD optimization, exploring predictive analytics for pipeline failure management, AI-enabled DevSecOps for integrated security, and performance enhancement through intelligent scheduling and resource allocation. The study further examines hidden technical debt in ML-enhanced pipelines, model interpretability challenges, and operational implications of automation. Through comprehensive theoretical elaboration and descriptive analysis, we propose a framework for AI-enhanced CI/CD pipelines that balances predictive capability, operational efficiency, and security resilience. Our findings highlight both the transformative potential and the limitations of AI-augmented software delivery systems, offering a roadmap for researchers and practitioners seeking to implement intelligent, secure, and adaptive CI/CD pipelines.

CI/CD pipelines, Machine Learning, predictive analytics

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