The integration of predictive analytics into DevOps frameworks marks a significant transition from reactive to proactive software lifecycle management. Traditional DevOps focuses on automation and integration; however, as system complexity grows, manual intervention and retrospective monitoring often fall short. This review explores how machine learning (ML) and statistical modeling—the core of predictive analytics—empower DevOps pipelines to anticipate failures, optimize resource allocation, and enhance security. By analyzing historical telemetry, deployment logs, and performance metrics, intelligent automation systems can forecast potential bottlenecks before they manifest as downtime. This article examines the architectural shift toward \"AIOps,\" the specific algorithms driving these advancements, and the measurable impact on key performance indicators like Mean Time to Recovery (MTTR) and Change Failure Rate. Ultimately, the synthesis of data science and operational excellence provides a roadmap for building self-healing, resilient infrastructure capable of meeting the demands of modern, large-scale digital environments.