The exponential growth of digital data and the increasing sophistication of anti-forensic techniques have pushed traditional Digital Forensics and Incident Response (DFIR) methodologies to their breaking point. Modern investigators are frequently overwhelmed by the sheer volume of logs, memory dumps, and disk images generated during a typical security breach. This review examines the paradigm shift toward Machine Learning (ML)-based DFIR, which leverages automated pattern recognition to accelerate the identification of malicious artifacts and reconstruct attack timelines. By utilizing supervised learning for malware classification, unsupervised learning for anomaly detection in system logs, and Natural Language Processing (NLP) for parsing unstructured forensic data, ML models provide a \\\"force multiplier\\\" for human investigators. This article categorizes current methodologies, focusing on deep learning for automated image forensics, clustering for identifying lateral movement in network telemetry, and recurrent neural networks for temporal event correlation. We explore how ML mitigates \\\"investigator fatigue\\\" by filtering noise and highlighting high-probability evidence, thereby significantly reducing the Mean Time to Detect (MTTD) and Mean Time to Remediate (MTTR). Furthermore, the review addresses critical challenges, including the \\\"black-box\\\" nature of deep neural networks, the legal admissibility of AI-generated evidence, and the emerging threat of adversarial machine learning. By synthesizing recent academic breakthroughs and industrial case studies, this paper provides a strategic roadmap for the development of \\\"Autonomous Forensics.\\\" The findings suggest that the integration of ML is not merely an efficiency gain but a fundamental requirement for maintaining digital justice and enterprise resilience in an increasingly complex and adversarial digital landscape.