The rapid escalation of global data traffic, catalyzed by the proliferation of 5G, Internet of Things (IoT) devices, and high-definition streaming services, has rendered traditional network management techniques increasingly obsolete. Packet Flow Classification serves as the foundational mechanism for Quality of Service (QoS) provisioning, resource allocation, and security enforcement. Historically, flow classification relied on port-based analysis or Deep Packet Inspection (DPI); however, the widespread adoption of end-to-end encryption protocols, such as TLS 1.3 and QUIC, alongside dynamic port allocation, has nullified these legacy methods. This review examines the paradigm shift toward Machine Learning (ML) and Deep Learning (DL) models as the primary engines for real-time traffic classification. By focusing on statistical flow features and byte-level patterns rather than plaintext payloads, ML models can identify applications and malicious intent within encrypted tunnels with unprecedented accuracy. We categorize current methodologies, ranging from classical supervised learners like Random Forests to advanced neural architectures, including Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) for temporal sequence modeling. Furthermore, the review addresses the critical challenges of real-time processing at line speed, data imbalance in network datasets, and the necessity for Explainable AI (XAI) in network operations. By synthesizing recent academic breakthroughs and industrial implementations, this paper provides a strategic roadmap for building autonomous, \"self-driving\" networks. The findings suggest that ML-driven packet flow classification significantly enhances network visibility and resilience, providing the cognitive intelligence required to manage the complex, opaque traffic landscapes of the modern digital era.