AWS Cloud Environments support core workloads and services, but are exposed to malicious actions and unauthorized activities in the transmission of network flow data. The threats subject cloud infrastructures to different types of attacks, thus Intrusion Detection in AWS Cloud Environments ensures privacy, reliability, and availability. This study explores the use of Machine Learning for intrusion detection by analyzing traffic patterns in cloud systems. The CSE-CIC-IDS2018 dataset, containing realistic benign and attack traffic, was employed for model training and evaluation. After comprehensive preprocessing and analysis, five Machine Learning algorithms were implemented: Random Forest, Decision Tree, Ridge Classifier, Logistic Regression, and Linear Support Vector Classifier. Their performance was measured using accuracy, precision, recall, F1 score, ROC-AUC, and detection time. Results showed that Random Forest and Decision Tree achieved the highest accuracy at 100%, with the Decision Tree demonstrating superior efficiency by classifying all instances in 0.056 seconds. Ridge Classifier followed with an accuracy of 99.2%, while Logistic Regression achieved 98.8%. The Linear Support Vector Classifier recorded the lowest performance with 96.2% accuracy. This research confirms the effectiveness of Machine Learning for cloud security. The Decision Tree Classifier, combining flawless accuracy with the fastest detection speed, emerges as the most practical model for real-time intrusion detection in AWS environments.