Continuous Integration and Continuous Deployment (CI/CD) pipelines form the foundation of modern software delivery. However, ensuring reliable and efficient deployments has become increasingly difficult due to growing system complexity, large-scale operations, and heterogeneous cloud-native environments. Conventional CI/CD approaches predominantly depend on reactive monitoring and static rule-based controls, which are often inadequate for anticipating failures and identifying performance risks in dynamic deployment scenarios. To overcome these limitations, this paper proposes PA-ML-CI/CD, a predictive analytics framework driven by machine learning that proactively evaluates deployment reliability and performance risks before pipeline execution. The framework integrates multi-source CI/CD telemetry, scalable feature engineering techniques, ensemble learning models, and a risk-aware deployment orchestration mechanism to enable intelligent and preventive deployment decisions. This study highlights the importance of embedding predictive machine learning and risk-aware decision-making capabilities into CI/CD pipelines to enable proactive, self-adaptive software delivery for large-scale cloud-native ecosystems.