Cloud environments, and Amazon Web Services (AWS) in particular, host high-value data assets and mission-critical workloads that make them attractive targets for Advanced Persistent Threat (APT) actors. Because forensic investigation techniques are applied only after a breach has already been discovered, the volume and velocity of cloud-generated telemetry make proactive, automated detection capabilities essential. This paper reviews machine learning-driven anomaly detection paradigms — supervised, unsupervised, semi-supervised, and deep learning — and examines their suitability for APT detection in AWS environments. It also reviews the AWS shared-responsibility security architecture, including Identity and Access Management (IAM), encryption services, and logging and monitoring services such as AWS Cloud Trail, AWS Config, Amazon Guard Duty, Amazon Detective, and Amazon Inspector, and considers the NIST Cyber security Framework (CSF) as a governance overlay that connects these technical capabilities to organizational risk management. Drawing on this review of the peer-reviewed and primary-source literature, the paper argues that no single detection paradigm is likely sufficient on its own, and that unsupervised and semi-supervised machine learning, combined with AWS-native security services and governed by the NIST CSF, offer a more resilient conceptual basis for cloud APT defense than any single method in isolation. On this basis, the paper proposes an Integrated Cloud APT Detection and Defense Model (ICADDM) as a conceptual architecture for researchers and practitioners, maps AWS security services against the MITRE ATT&CK Cloud Matrix, and identifies the empirical validation of the model against real cloud telemetry as the principal direction for future work.