Cloud threat detection and response technologies are at the forefront of maintaining cybersecurity amid increasingly dynamic and complex infrastructures. The technologies are programmed to identify, assess, and respond to likely threats in real time to provide cloud service integrity and availability. Static rule-based mechanisms and manual control mechanisms find it challenging to keep pace with evolving attack patterns, and this has necessitated the use of automated, intelligent solutions. This paper explores the employment of autonomous threat detection and response systems using artificial intelligence (AI) and machine learning (ML). The binary classification model was used to identify harmless and threat-related network traffic from a Kaggle-based DDoS dataset. The data underwent rigorous preprocessing, exploratory data analysis, and feature engineering. Five machine learning (ML) models were trained and evaluated against performance measures like accuracy, precision, F1-score, and detection time. The Decision Tree model gave better performance, with a high accuracy of 98.0% and real-time capability. Its integration into cloud infrastructures allows for self-healing, adaptive cybersecurity defenses.