The modern IT (Information Technology) infrastructure uses cloud computing as its cornerstone to provide quick and simple access to physical and virtual resources like CPU (Central Processing Unit), memory, and storage capabilities. The issue with power efficiency in cloud CPUs remains difficult to solve because cloud workloads maintain constant configuration changes. Standard resource allocation strategies create multiple performance issues by allocating excessive or insufficient resources that raise costs and reduce system quality. Real-time adaptive CPU utilization becomes achievable through machine learning (ML) technology, which provides an effective solution against these current difficulties. The CPU becomes more efficient because of machine learning. The CPU learns improved and faster task execution through training that analyzes data from mistakes to enhance its performance for specific responsibilities. The CPU shows adaptability for continuous changes while making immediate better decisions thus offering an effective base to solve these problems. This research investigates how machine learning (ML) can help the CPU use resources adaptively in real time. Using analyzing operational data, like core use, how many times the system starts, and its temperature, random forests, recurrent neural networks (RNN), long short-term memory (LSTM) and convolutional neural networks (CNN) can assist in training ML models that enhance prediction performance and optimize CPU activities. Analysis in this paper investigates multiple approaches for improving CPU efficiency in cloud computing networks, research constraints, and forthcoming opportunities resulting in heightened CPU resource functionality and reduced energy utilization.