The need for precise prediction systems and effective traffic management has grown as a result of the digital networks' explosive expansion. Applying SCA to prediction and network traffic management systems is the main objective of this paper [1]. Scalable, reusable, and readily maintainable components for anomaly detection, predictive modeling, and real-time traffic analysis can be created and implemented using SCA. The SCA framework improves its capacity to forecast future traffic patterns by utilizing machine learning techniques, which permits proactive resource allocation and congestion alleviation