Summary-Extremely dynamic, cloud native ecosystems especially those based on microservicesarchitecture feature intricate interdependencies and constant load and resource fluctuations. These environments often have unanticipated problems that lead to worse performance, outages, and higher running expenses. Because conventional monitoring systems find it difficult toforecast and prevent such occurrences in Realtime, adopting more proactive strategies is vital. This work offers a defect prediction method driven by artificial intelligence combined withAIOps to actively identify and avoid faults in cloud native systems. The model uses machinelearning methods to examine past system data, forecast possible failures, and start automatedcorrective steps such resource scaling or traffic rerouting. Experimental findings showthat thesuggested method greatly minimizes system downtime, increases the precision of problemdetection, and optimizes resource consumption. This work, which presents a unique approachforincluding machine learning-based defect prediction into AIOps systems, provides a moreautomated and efficient tool for controlling system health in dynamic cloud native settings.