The rapid evolution of cloud-native computing and microservices architectures has introduced unprecedented complexity into the software development lifecycle, necessitating a paradigm shift from traditional manual operations to automated, intelligent systems. This research provides an exhaustive exploration of the integration of Artificial Intelligence (AI) and Machine Learning (ML) within the DevOps and Site Reliability Engineering (SRE) domains. By synthesizing foundational principles of AI-driven continuous testing, proactive auto-scaling, and automated incident management, the study delineates a framework for achieving agile excellence. We examine the transition from DevOps to Machine Learning Operations (MLOps), identifying the architectural requirements for maintaining distributed edge and container-based services. Furthermore, the research investigates the prioritization of security challenges using multi-criteria decision-making models and evaluates the efficacy of memory leak and deadlock detection in distributed systems. Through a systematic analysis of current research trends, this article highlights the critical role of observability and quality-aware research in the container age. The findings suggest that the integration of ensemble models for predictive scaling and AI-based threat detection significantly enhances software quality and operational reliability. This article concludes with a roadmap for future research, emphasizing the need for unified full-stack environments and agile network access control to mitigate the inherent risks of modern cloud-hosted applications.