The evolution of software engineering has been profoundly influenced by the integration of artificial intelligence (AI) into operational frameworks, particularly within DevOps practices. AI-driven DevOps, commonly termed AIOps, represents a paradigm shift, offering intelligent automation for deployment, maintenance, monitoring, and predictive analytics. This study provides a comprehensive investigation into the theoretical foundations, practical implementations, and emerging challenges associated with AI integration in DevOps. Drawing from machine learning (ML) methodologies, neural architecture optimization, and statistical anomaly detection, the research situates AI-augmented operations within the broader landscape of contemporary software engineering. By synthesizing findings from recent empirical studies and case analyses, including predictive maintenance in industrial IoT and automated log anomaly detection, the study illuminates the operational, ethical, and strategic considerations central to AI-driven DevOps. Additionally, the paper explores the complexities of explainable AI (XAI) within deployment pipelines, highlighting the tension between model performance and interpretability, as well as the technical debt accumulated in machine learning systems. Through critical discussion, this research outlines a roadmap for optimizing AI integration in software operations, balancing efficiency, reliability, and fairness. The study concludes with reflections on the scalability of AI-driven processes, the mitigation of biases, and future directions for research in adaptive, autonomous software management systems.