The unprecedented escalation in global data traffic, driven by 5G expansion, hyperscale cloud computing, and the Internet of Things (IoT), has fundamentally altered the threat landscape for high-speed networks. Traditional Network Intrusion Detection Systems (NIDS) that rely on manual signature matching or basic statistical thresholds are increasingly incapable of processing traffic at terabit-per-second scales, leading to significant visibility gaps. This review examines the paradigm shift toward Machine Learning (ML)-based anomaly detection as a solution to the \"data deluge\" in high-speed environments. By focusing on flow-level metadata and statistical behavioral patterns rather than computationally expensive deep packet inspection (DPI), ML models can identify malicious intent within microseconds. We categorize current methodologies, ranging from unsupervised clustering for zero-day discovery to deep learning architectures like Convolutional Neural Networks (CNNs) for spatial traffic analysis and Long Short-Term Memory (LSTM) networks for temporal sequence modeling. This article explores how these models mitigate \"alert fatigue\" by providing high-precision filtering of benign noise while identifying subtle \"low and slow\" adversarial tactics. Furthermore, the review addresses the critical challenges of real-time inference at the network edge, the necessity for model quantization to fit within limited hardware buffers, and the emerging risk of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building \"Cognitive Defense\" systems. The findings suggest that ML-integrated anomaly detection is the only viable mechanism for maintaining network resilience and integrity in an increasingly automated and high-velocity digital ecosystem.