This paper’s primary goal is to support businesses in growing and meeting customer expectations in real time. The domain of high-performance computing has become increasingly significant for computer technology and businesses. Numerous corporate organizations and high-end tech companies are working together to develop ways to increase the system's efficiency and resilience to traffic so that it may be used all day. Machine learning is one of the newest and most significant developments in computing technology. This uses historical data to inform classification and prediction algorithms that help in decision-making. In this paper, we present and incorporate the idea of using machine learning and strong computing methods to connect cloud platforms. The system efficacy and continuous supply of traffic resilience decisions are ensured, as well as the evaluation, prediction, and classification of traffic and computation structures, using the networking and computer performance data. The many steps and choices in the suggested integrated design approach have been investigated through the use of machine learning regression and classification models that dynamically adjust the system's performance at actual run times. When compared to non-machine knowledge based architectural models, the machine knowledge-based simulation results of the design show that traffic resiliency operates successfully 38.15% faster in terms of failure site recovery as well as 7.5% less expensive.