Cloud Computing-Driven Machine Learning Strategies for Predicting and Preventing Cyber Threats

The modern security systems experience difficulties in managing rising cyber threats because traditional security solutions require better capabilities to detect new attack methods. The investigation establishes the potential alignment between cloud computing and machine learning technology to build security solutions that adapt intelligently. Cloud computing establishes a flexible system which manages substantial real-time security data effectively while ML provides robust analytical methods for both identifying patterns and detecting anomalies and making predictive assessments. This document analyzes multiple ML methods consisting of supervised, unsupervised and reinforcement learning to identify phishing incidents along with malware and denial-of-service attacks and insider dangers found in cloud systems. The paper analyzes model evaluation metrics which include accuracy alongside precision and recall and false positive rate to measure operational efficiency. This paper evaluates the execution obstacles that occur in cloud-based systems including resource distribution issues and data security concerns with latency problems together with model movement problems. This research demonstrates the benefits of taking ML models into cloud deployment because it boosts the capabilities of cyber threat prediction and prevention to build efficient proactive defense systems. The designed framework delivers instant threat information and operates automatic response methods which build cyber defense capabilities across entire systems. The presented investigation adds new capabilities to security architectures built for cloud environments that defend against contemporary cyber dangers.